Prediction of turbofan engine characteristic

Abstract

To determine physical characteristics of a turbofan engine.SOLUTION: In a memory, (i) a computer support design (CAD) model indicating a turbofan engine, and (ii) display of a flow condition are acquired. Subsequently, a solver input file is automatically determined on the basis of display of the CAD model and the flow condition. Simulation of the turbofan engine subjected to the flow condition by the response is performed using the determined solver input file. The result of the simulation indicates physical characteristics of the turbofan engine.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to predicting turbofan engine characteristics. [Background technology]

[0002] Many existing products and simulation systems are available on the market for designing and simulating objects, such as vehicles. These systems typically employ computer-aided design (CAD) and computer-aided engineering (CAE) programs. These systems allow users to build, manipulate, and simulate complex three-dimensional models of objects or assemblies of objects. These CAD and CAE systems provide model representations of objects, such as real-world objects, using edges or lines, and in certain cases, edges or lines with faces. The lines, edges, faces, or polygons may be represented in various ways, such as, for example, non-uniform rational B-splines (NURBS).

[0003] Such systems manage parts or assemblies of parts of a modeled object, primarily specifications of the shape. In particular, a CAD file contains the specifications from which the shape is generated. From the shape, a three-dimensional CAD model or model representation is generated. The specification, shape, and CAD model / representation may be stored in a single CAD file or multiple CAD files. CAD systems or other such CAE systems include graphical tools to visually represent the modeled object as it appears in three-dimensional space to the designer; these tools are specialized for displaying complex real-world objects. For example, an assembly may contain thousands of parts.

[0004] The advent of CAD and CAE systems has enabled a wide range of representation possibilities for objects, such as CAD models. Computer-based models may be programmed to have the properties (e.g., physical, material, or other physics-based) of the underlying real-world object they represent. Exemplary properties include stiffness (ratio of force to displacement), plasticity (irreversible strain), and viscosity (resistance to flow of one layer over an adjacent layer), among others. When a CAD model or other such computer-based model known in the art is programmed in such a manner, it can be used to perform a simulation of the object it represents. For example, a mesh-based model may be used to represent the interior cavity of a vehicle, an acoustic fluid surrounding a structure, or any number of real-world objects / systems. Furthermore, CAD and CAE systems, along with computer-based models, may be utilized to simulate real-world physical systems, e.g., engineering systems such as automobiles, airplanes, buildings, and bridges, among other examples. Furthermore, CAE systems can be used to simulate any variety and combination of the behavior of these physics-based systems, such as noise and vibration. Summary of the Invention

[0005] Transportation and commuting are areas of concern as the population continues to grow. 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 for accurately determining the characteristics of such turbofans, such as their physical and operational characteristics, are needed to realize their full potential and optimize their design. Embodiments provide a solution to this problem.

[0006] An illustrative embodiment is directed to a computer-implemented method for determining physical characteristics of a turbofan engine. According to one embodiment, the method begins by acquiring, by a processor, in a memory coupled to the processor, (i) a computer-aided design (CAD) model representing the turbofan engine and (ii) a representation of flow conditions. Solver input files are then automatically determined based on the CAD model and the representation of the flow conditions. In response, a simulation of the turbofan engine subjected to the flow conditions is performed using the determined solver input files. Results of the simulation are indicative of the physical characteristics of the turbofan engine.

[0007] In one embodiment, the processor implementing the method is one of a plurality of processors supporting a global network platform service. In another embodiment, the CAD model and the representation of flow conditions are obtained in response to user input via a user interface of the platform service. According to one embodiment, the flow conditions include at least one of freestream airspeed, fan rotation speed, and air temperature.

[0008] In yet another embodiment, the determined solver input file includes at least one of a surface mesh, a measured surface, and a representation of flow conditions. According to one embodiment, determining the solver input file includes at least one of creating a surface mesh and creating a measured surface within the surface mesh. In one embodiment, creating the surface mesh includes analyzing the CAD model to determine at least one geometric parameter: engine bounding box dimensions, nacelle leading and trailing edge location and shape, fan diameter, fan blade count, fan tip gap size, fan blade leading and trailing edge shape, outlet guide vane location, outlet guide vane leading and trailing edge shape, low-pressure compressor stage location (if present), low-pressure compressor stage blade count (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, embodiments may also identify portions of the turbofan engine based on the names of each of the components in the CAD model representing the identified part. Embodiments may create a surface mesh based on at least one of the determined geometric parameters and the identified parts.

[0009] According to one embodiment, performing the simulation includes generating a volumetric mesh based on the surface mesh and performing the simulation using the generated volumetric mesh. Further, embodiments may set a local mesh resolution for at least a portion of the volumetric mesh and set a volumetric mesh resolution region based on dimensions and shapes of elements in the surface mesh.

[0010] 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 position of a measurement surface based on a representation of the flow conditions and the size and shape of elements in the 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.

[0011] According to one embodiment, performing the simulation includes at least one of performing a computational fluid dynamics (CFD) simulation, e.g., a 3D CFD simulation, and a Lattice Boltzmann Method (LBM) simulation. Further, in one embodiment, the determined physical properties include at least one of aerodynamic properties, thermal properties, and acoustic properties.

[0012] Embodiments may further include (i) generating a two-dimensional (2D) mesh from the solver input file; and (ii) using the results of the simulation and the generated 2D mesh, performing multiple finite element method (FEM) simulations, each FEM simulation performed using a respective flow condition and a representation of a respective liner in the generated 2D mesh to determine noise reduction characteristics of the respective liner.

[0013] Another embodiment is directed to a system for determining physical characteristics of a turbofan engine. In one embodiment, the system includes a processor and a memory having computer code instructions stored thereon, the processor and the memory configured to use the computer code instructions to cause the system to implement any embodiment or combination of embodiments described herein.

[0014] Yet another is directed to determining physical characteristics of a turbofan engine, the system including a processor and a memory having computer code instructions stored thereon, the processor and memory configured to use the computer code instructions to cause the system to implement a platform service configured to perform any embodiment or combination of embodiments described herein.

[0015] In another embodiment, a computer program product includes one or more non-transitory computer-readable storage devices having computer-readable program instructions stored thereon that, when executed by a processor, are configured to cause a device associated with the processor to implement any embodiment or combination of embodiments described herein.

[0016] Another embodiment includes an automated process to rapidly generate a 3D turbofan engine digital twin, carry out fluid dynamic simulations with different operating conditions, and post-process the results to evaluate the turbofan's aerodynamic performance and far-field acoustic / ground noise levels.

[0017] Another aspect involves automatically generating a 2D mesh from a 3D turbofan engine geometry, which can then be used for reduced-scale acoustic evaluation of the nacelle in the presence of passive noise reduction devices such as liners.

[0018] Yet another aspect includes extracting a selected data set (e.g., a transient pressure field) from a 3D turbofan engine simulation and using the selected data set along with liner impedance characteristics to perform a series of reduced-size acoustic simulations to rapidly evaluate noise reduction that may be achieved by placing one or more passive liners within the turbofan (e.g., a turbofan nacelle).

[0019] Embodiments may preprocess the engine geometry using a tool (i.e., software component, application, etc.) that can read and automatically extract certain geometric characteristics of the engine. In one aspect, the tool is also configured to automatically generate measurement surfaces and other virtual entities used to store data for use in far-field sound evaluation.

[0020] Embodiments may also implement and employ tools that can evaluate the far-field noise of an engine using specific validation metrics and computational methodologies.

[0021] Other aspects include computer program products tangibly stored on non-transitory computer-readable media, and computing systems such as computer systems and computer servers.

[0022] An exemplary embodiment implements a workflow in which a user first prepares an engine geometry (e.g., a surface mesh) according to a set of guidelines that specify a specific nomenclature to use for naming relevant engine surfaces. This nomenclature enables such embodiments to automatically identify each engine section, specify volumetric mesh resolution, and generate additional mesh entities for collecting results. Additionally, in yet another embodiment, a user provides one or more sets of flow states (operating conditions) for analyzing a target turbofan.

[0023] With these two inputs (engine geometry with nomenclature-compliant elements and flow conditions), the noise prediction process begins. In one embodiment, the process occurs in three stages: Stage 1: Analyze the engine model (surface mesh) and generate solver input files, along with flow conditions provided by the user. Stage 2: Simulations run automatically on a local or remote cluster, and multiple simulations can be run simultaneously (e.g., to evaluate different flow condition scenarios). Stage 3: The simulation results obtained from Stage 2 are processed, and a dataset is generated for each of the submitted cases.

[0024] The implementation runs on a cloud-based platform, and in one embodiment, such cloud-based platform includes an interactive environment for viewing and comparing datasets.

[0025] In a further aspect, an embodiment can generate a reduced model for noise reduction analysis for a selected case.

[0026] It should be noted that the methods, systems, and computer program product embodiments may be configured to implement any embodiment or combination of embodiments described herein.

[0027] The foregoing will be apparent from the following more particular description of exemplary embodiments, as illustrated in the accompanying drawings, in which like reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating embodiments. [Brief explanation of the drawings]

[0028] [Figure 1] FIG. 1 is a flowchart of a method for determining physical characteristics of a turbofan, according to one embodiment. [Figure 2]FIG. 2 is a block diagram illustrating a Platform as a Service (PaaS) system in which embodiments may be implemented. [Figure 3] FIG. 3 is a flow chart illustrating a method for determining physical characteristics of a turbofan, according to one embodiment. [Figure 4A] 4A-C show turbofan layouts that may be analyzed using embodiments. [Figure 4B] 4A-C show turbofan layouts that may be analyzed using embodiments. [Figure 4C] 4A-C show turbofan layouts that may be analyzed using embodiments. [Figure 5A] 5A-D illustrate turbofan geometries that may be utilized in embodiments. [Figure 5B] 5A-D illustrate turbofan geometries that may be utilized in embodiments. [Figure 5C] 5A-D illustrate turbofan geometries that may be utilized in embodiments. [Figure 5D] 5A-D illustrate turbofan geometries that may be utilized in embodiments. [Figure 6A] 6A-D show exemplary turbofan characteristics determined using an embodiment. [Figure 6B] 6A-D show exemplary turbofan characteristics determined using an embodiment. [Figure 6C] 6A-D show exemplary turbofan characteristics determined using an embodiment. [Figure 6D] 6A-D show exemplary turbofan characteristics determined using an embodiment. [Figure 7A] 7A-C show exemplary turbofan characteristics determined using an embodiment. [Figure 7B] 7A-C show exemplary turbofan characteristics determined using an embodiment. [Figure 7C] 7A-C show exemplary turbofan characteristics determined using an embodiment. [Figure 8A] 8A-B show exemplary turbofan characteristics determined using an embodiment. [Figure 8B] 8A-B show exemplary turbofan characteristics determined using an embodiment. [Figure 9A] 9A-B show exemplary turbofan characteristics determined using an embodiment. [Figure 9B] 9A-B show exemplary turbofan characteristics determined using an embodiment. [Figure 10A] 10A-B show exemplary turbofan characteristics determined using an embodiment. [Figure 10B] 10A-B show exemplary turbofan characteristics determined using an embodiment. [Figure 11] FIG. 11 illustrates a method for noise analysis, according to one embodiment. [Figure 12] FIG. 12 is a simplified block diagram of a computer system embodiment for determining physical characteristics of a turbofan. [Figure 13] FIG. 13 is a simplified block diagram of a computer network environment in which embodiments of the present invention may be implemented. DETAILED DESCRIPTION OF THE INVENTION

[0029] A description of an exemplary embodiment follows.

[0030] Embodiments provide functionality for determining the physical characteristics of a turbofan. Among other examples, embodiments may provide a community noise assessment of a turbofan engine via a fully automated process executed on a support services platform.

[0031] A turbofan is an aircraft engine designed to operate at low subsonic or transonic cruise speeds. Turbofan engines generate thrust by accelerating a volume of air through the engine. A portion of the ingested air undergoes compression, combustion, and expansion (core flow), while another portion of the ingested air is accelerated by the engine fan without combustion (bypass flow). Recent low-emissions requirements have driven new engine designs that increase the amount of bypass air, which has led to the use of larger fans. The transition to larger fans has shifted the primary noise source from the jet to the engine fan and outlet guide vanes (OGVs), which are mounted immediately downstream of the fan and straighten the nacelle exit flow.

[0032] For this reason, it is now critical to be able to capture the complex interactions between the fan and OGV through simulation, especially in order to accurately predict the noise generated by the engine and its propagation to the ground, necessitating the high cost and risk of physical testing and long turnaround times (TAT) for design iterations. This is especially true during takeoff and landing, where regulations set precise noise thresholds that, if not met, can significantly limit aircraft maneuverability.

[0033] Accurate determination of turbofan characteristics, such as noise, requires simulations that reproduce the fan wake in its full time and spatial complexity. Embodiments provide this functionality. Exemplary embodiments implement a transient computational fluid dynamics (CFD) solver capable of handling realistic engine geometries. Furthermore, low numerical dissipation is desirable for aeroacoustic analysis to reduce numerical damping of pressure waves as much as possible. For this reason, embodiments may employ a Lattice Boltzmann Method (LBM)-based solver for aeroacoustic analysis.

[0034] FIG. 1 is a flowchart of an exemplary method 100 for determining physical characteristics of a turbofan. Method 100 begins at step 110 by a processor obtaining, in a memory coupled to the processor, (i) a computer-aided design (CAD) model representing a turbofan engine and (ii) a representation of flow conditions. Then, at step 102, solver input files are automatically determined based on the CAD model and the representation of flow conditions. In response, at step 103, a simulation of the turbofan engine subjected to the flow conditions is performed using the determined solver input files. In one embodiment, performing the simulation at step 103 includes submitting the simulation at a large data center using multiple processors organized into compute nodes that can handle the large memory required and generate results within a reasonable timeframe. Results of the simulation performed at step 103 are indicative of the physical characteristics of the turbofan engine.

[0035] As described above, method 100 is computer-implemented, such that functions and operations, e.g., steps 101-103, may be implemented automatically by one or more digital processors. Furthermore, method 100 may be implemented using any computer device or combination of computing devices known in the art. Among other examples, method 100 may be implemented using computer system 1220, described herein below in connection with FIG. 12, and computer network environment 1330, described herein below in connection with FIG. 13.

[0036] Additionally, method 100 may 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. Dassault Systemes' 3DEXPERIENCE® platform is described herein as an example of an environment that can provide data management and process automation tools for implementing embodiments, e.g., method 100. The use of this type of environment (PaaS) may be essential to allow users to view the large amounts of data that make up a digital twin of a given product. A digital twin may be a unified dataset containing geometric, material, and ancillary data for a given product (e.g., a turbofan), and thus a PaaS may be utilized to store, manage, process, and evaluate the digital twin using method 100.

[0037] In a PaaS implementation embodiment of method 100, the processor implementing method 100 may be one of multiple processors supporting a global network (i.e., Internet) platform service. Note that embodiments are not limited to implementation on a global network; embodiments may be implemented on any network known to those skilled in the art, including local area networks (LANs) and wide area networks (WANs). Still further, method 100 may be implemented in a PaaS environment 220 shown in FIG. 2. Additionally, one embodiment utilizes SIMULIA PowerFLOW® by applicant-assignee Dassault Systemes Americas Corporation to implement an automated workflow for determining turbofan characteristics, such as noise assessment.

[0038] In one embodiment, the CAD model obtained in step 101 is a three-dimensional CAD model that includes all relevant parts of the turbofan. For example, the model obtained in step 101 may include a core component, outlet guide vanes (OGVs), and a fan mounted on an engine nacelle. Furthermore, in embodiments, the CAD model obtained in step 101 may be any computer-based model known in the art. As such, embodiments of method 100 may perform additional processing to bring the model received in step 101 into a desired form, such as a 3D CAD model. An exemplary CAD model 550 that may be obtained in step 101 is described herein below in connection with FIG. 5B. Furthermore, in one embodiment, each element of the CAD model received in step 101 is named according to a specific nomenclature. In such an embodiment, utilizing this nomenclature allows for automatic identification of each engine section.

[0039] The indication of flow conditions obtained in step 101 may be in any form that can be processed and identified by a computing device implementing method 100. The indication received in step 101 may indicate any flow condition known to those skilled in the art. For example, according to one embodiment, the flow conditions include at least one of freestream airspeed, fan rotation speed, and air temperature.

[0040] Additionally, in one embodiment, the CAD model and flow conditions are obtained in step 101 in response to user input, for example, via a user interface of a platform service on which method 100 is implemented.

[0041] To automatically determine the solver input file in step 102 based on the CAD model and representation of the flow conditions, one embodiment determines a surface mesh of the CAD model (obtained in step 101) and determines the solver input file based on the determined surface mesh. In one embodiment, the surface mesh is generated from the CAD more or less automatically (a generally refined surface mesh where high surface curvature is found). The solver input file includes definitions of regions where a volumetric mesh (which may be generated in step 103 as part of performing the simulation) will be refined based on the flow conditions and the location / dimensions and shape of the engine parts. In embodiments where the volumetric mesh is generated in step 103 based on the solver input file, the volumetric mesh is generated automatically when the simulation is submitted.

[0042] According to one embodiment, the solver input file is determined in step 102 by generating a 3D computational mesh in a fully autonomous manner. Furthermore, in one embodiment, the solver input file includes data including a turbofan surface mesh and specified flow conditions. An embodiment of method 100 determines the characteristics of the solver input file based on characteristics of the turbofan engine model obtained in step 101. To perform these functions, one embodiment analyzes the model's geometry, e.g., the gap between the nacelle and the fan, and sets simulation parameters, e.g., minimum resolution, from the analysis. The flow conditions can also be used to derive modifications to the model, e.g., to analyze different operating conditions, such as approach, takeoff, and cruise. In another embodiment, the received data, e.g., the data obtained in step 101, includes a frequency range of interest, which 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 a sampling frequency for the simulation results.

[0043] In yet another embodiment, the solver input file determined in step 102 includes at least one of a surface mesh, a measured surface, and a representation of flow conditions. According to such an embodiment, determining the solver input file in step 102 may include creating a surface mesh and / or creating a measured surface within the surface mesh. In one embodiment, creating the surface mesh includes analyzing the CAD model (obtained in step 101) to determine geometric parameters. According to one embodiment, the determined geometric parameters may include analyzing the CAD model to determine at least one of the following geometric parameters: engine bounding box dimensions, nacelle leading and trailing edge location and shape, fan diameter, fan blade count, fan tip gap size, fan blade leading and trailing edge shape, outlet guide vane location, outlet guide vane leading and trailing edge shape, low-pressure compressor stage location (if present), low-pressure compressor stage blade number (if present), low-pressure compressor stage leading and trailing edge shape, and low-pressure compressor rotor stage fan tip gap size (if present). From 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 plane used to calculate far-field noise; the fan tip gap can set the minimum resolution of the 3D volume mesh (which is automatically generated from the input file when the 3D simulation is submitted); the position and shape of the fan and outlet guide vanes can define the measurement plane used to obtain stage performance and flow characteristics; the fan blade count can define the blade passing frequency (BPF) of the turbofan; and the blade count of the fan and compressor (if present) can define the time sampling used to generate a phase-locked average of the flow characteristics. As part of creating the surface mesh, embodiments of method 100 can also identify parts of the turbofan engine based on the names of each of the components in the CAD model (received in step 101) representing the identified part.A surface mesh is then generated from these identified parts and / or determined geometric parameters. This surface mesh may be generated by setting a spatial tolerance to be observed, which measures the distance of each surface mesh element from the original CAD surface. This generates a locally refined surface mesh where high surface curvature is found. Once the solver input file is generated, the simulation may be submitted and a 3D volume mesh may be automatically generated from the surface mesh and simulation settings. When creating the input file, embodiments of method 100 may also set a local mesh resolution for at least a portion of the computational domain.

[0044] According to one embodiment of method 100, performing the simulation includes at least one of performing a computational fluid dynamics (CFD) simulation and a lattice Boltzmann method (LBM) simulation in step 103.

[0045] According to one embodiment, performing the simulation in step 103 includes generating a volumetric mesh based on the surface mesh and performing the simulation using the generated volumetric mesh. Further, embodiments may set a local mesh resolution for at least a portion of the volumetric mesh and set a volumetric mesh resolution region based on dimensions and shapes of elements in the surface mesh.

[0046] In another aspect, performing the simulation in 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 plane based on a representation of the flow conditions and the size and shape of the elements in the surface mesh, for example, the size and shape of the engine part (the inlet and outlet intersection planes of the engine stage may be defined based on the fan and outlet guide vane positions 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 performed 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).

[0047] An embodiment of method 100 may determine any physical characteristic of the turbofan known to one of ordinary skill in the art at step 103. For example, according to one embodiment, the determined physical characteristic includes at least one of aerodynamic characteristics, thermal characteristics, and acoustic characteristics.

[0048] Embodiments of method 100 can also be utilized to evaluate turbofan noise reduction through the use of aeroacoustic liners. Aeroacoustic liners are passive devices primarily used to reduce noise generated by engine fans. In their most basic layout, aeroacoustic liners consist of a series of hollow cells covered by perforated plates that absorb and reduce pressure fluctuations generated by the engine fan, which directly contribute to its noise generation. Liners are typically positioned along the nacelle inlet section (upstream of the fan), between the fan and the OGV, and downstream of the OGV. In one such exemplary embodiment of method 100, a two-dimensional (2D) mesh is generated from the solver input file (determined in step 102). Such an embodiment may then perform multiple finite element method (FEM) simulations utilizing the simulation results (from step 103) and the generated 2D mesh. Each FEM simulation is performed using a respective flow condition and a representation of the respective liner within the generated 2D mesh. Performing the FEM simulations in this manner determines the noise reduction characteristics of each liner subject to each flow condition. These results can then be used, for example, to manufacture turbofans and liners that meet noise requirements.

[0049] 2 is a block diagram illustrating a PaaS 220 in which embodiments, such as methods 100 and 330 described herein, may be implemented. Platform 220 is a cloud platform that may be utilized to perform simulations of turbofan engines as described herein. While the description herein refers to PaaS 220 being the Dassault Systemes 3DXPERIENCE® platform, PaaS embodiments are not limited to being implemented using the 3DXPERIENCE® platform; instead, embodiments may be implemented using any PaaS known to those skilled in the art.

[0050] Platform, i.e., system 220, is based on a client-server or cloud-based architecture and includes a cloud server 221 and client systems 223 connected to a massively parallel computing cluster 222 (which can be standalone or cloud-based). Computing cluster 222 is communicatively coupled to platform storage 224, which is used to store data and software utilized by cluster 222. Among other examples, platform storage 224 includes CAD and 2D / 3D mesh data 225a, results data 225b, libraries 225c, and scripts 225d. Similarly, client systems 223 are communicatively coupled to client storage 226, which stores turbofan engine CAD data 227.

[0051] Cloud server 221 may include multiple instances 228a-n, each having a user interface 229, data / tools (CAD editor and mesh tools 230, digital twin, e.g., CFD solver input 231, processed results 232), a bus system 233, and a processing unit (collectively 234) with local memory. According to one embodiment, tools and data 230-232 are available to users of client systems 223 via interface 229 and bus 233. Processing and memory for providing access to and implementation of the tools are provided by processing unit 234.

[0052] During operation, a user of client system 223 accesses CAD model 227 (stored on storage 226) for a target turbofan and provides CAD model 227 to cloud instance 228a via interface 229. The user prepares and submits a simulation, e.g., an aeroacoustic simulation, in the form of a digital twin, i.e., solver input file 231, via interface 229. In one embodiment, simulation file 231 is prepared and submitted by modifying a set 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 the parameters, engine digital twin 231 includes a CAD file containing a digital representation of the entire engine (from CAD model 227) uploaded from client 223 and processed within instance 228a. This processing may include editing CAD model 227 and / or meshing CAD model 227 using editing and meshing tools 230. As part of simulation preparation and submission (e.g., during preprocessing, such as stage 1 described herein), the engine model (227) may be analyzed by one or more geometry preparation scripts 225d, which extract a set of geometric parameters (e.g., engine size, number of fan blades, fan tip / shroud gap size, etc.) used in the simulation setup. At the same time, post-processing parameters may be prepared and set to default values. The user may modify these parameters, if desired, to customize post-processing based on the user's needs via interface 229.

[0053] Once the digital twin is prepared, CFD input files 231 are provided to cluster 222, which performs the simulation. To implement these functions, cluster 222, powered by cloud instance 228a, accesses platform storage 224, which stores 2D and / or 3D meshes 225a, coordinate systems, and libraries 225c, to perform the simulation using any known computational technique, such as CFD or LBM. The simulation (e.g., stage 2 described herein) can be for various purposes, such as aeroacoustics, estimation of aerodynamic and thermal performance, acoustic noise generation, or the impact of noise reduction devices installed inside the engine nacelle. The results of the simulation 225b show the physical characteristics of the turbofan engine, represented by CAD model 227.

[0054] In one embodiment, once the simulation is run, post-processing script 225d can be launched automatically from instance 228a (e.g., during stage 3 as described herein) to generate results 225b (e.g., plots and images) based on selections made during pre-processing. The processed dataset (results 225b) can be stored in platform storage 224. A user can view the processed results via interface 229 by using visualization tools implemented by processed results browser 232.

[0055] Further, according to one embodiment, post-processing may include collecting pressure field data on the fan intake and OGV exit surfaces and storing the pressure field data as part of the results 225b after projecting the time domain field onto its respective radial and azimuthal mode components in the frequency domain.

[0056] Further, in one embodiment of platform 220, script 225d executes in processing unit 234, which accesses the geometric data in 231 and automatically generates a 2D mesh representing a vertical cut of the 3D engine (a half geometry is considered here). In such an embodiment, a portion of the nacelle inner surface may be configured as a liner. Such a 2D mesh may be stored with data 225a and used to build a reduced model to evaluate the noise reduction capabilities of the liner when installed in / on the engine. Such an embodiment may model the liner with a 2.5D simulation for pressure azimuthal modes in the frequency domain. This implementation may utilize a 3D engine mesh and a previously generated 2D mesh from a FEM solver (e.g., the OptydB_gfd FEM solver by applicant-assignee Dassault Systemes Americas Corporation), which may set a previously collected pressure data set from the 3D simulation as a forcing term and calculate the far-field pressure fluctuations generated by an engine with a nacelle from which the liner is created. In one embodiment, automation script 225d stored in platform storage 224 is invoked by instance 228a to run multiple FEM simulations for different frequency and fan pressure boundary conditions (azimuth / radiation modes). According to one embodiment, only engine cutoff mode is used. A second script 225d stored in platform storage 224 is executed by instances 228a-n to collect far-field sound results and generate plots showing noise levels across the frequency domain of interest.

[0057] 3 is a flow diagram illustrating a method 330 for determining physical characteristics of a turbofan, according to one embodiment. In FIG. 3, process 330 provides details of each processing step (e.g., steps 331-338). Among other examples, process 330 may be implemented within platform 220 described herein above in connection with FIG. 2.

[0058] Process 330 begins with geometry and input data at step 331. More specifically, at step 331, the geometry of, for example, a turbofan engine is obtained via user input and flow conditions are set up. Table 1 below shows input data that may be obtained at step 331 of process 330. These parameters may be set within a graphical user interface (GUI) of the cloud platform.

[0059] [Table 1]

[0060] To continue, in step 332, the geometry obtained in step 331 is processed, from which a surface mesh of the turbofan engine is created, and part names are assigned within the mesh according to guidelines. Further details regarding meshing and naming are provided herein below in connection with FIGS. 5A-D. To continue, in step 333, a solver input file is automatically generated using the flow conditions and the created surface mesh. According to one embodiment, the solver input file includes information regarding the flow conditions (e.g., all necessary information), the engine's surface mesh, measurement surfaces, and volumetric mesh resolution control. The solver input file generated in step 333 is then used in step 334 to perform one or more simulations of the turbofan engine. In an embodiment, multiple simulations can be performed simultaneously, for example, to identify characteristics of turbofans with different geometries and / or to identify characteristics of turbofans subjected to different flow conditions. In step 335, post-processing is performed on the results of the simulations performed in step 334. The post-processing in step 335 can include air and thermal data processing and aeroacoustic processing. Among other examples, the aeroacoustic processing can utilize specialized tools (e.g., software tools) to determine far-field sound / ground noise levels. Table 2 below lists input parameters that may be obtained in step 335 to perform post-processing. These parameters can be set by a user, for example, within the cloud platform GUI.

[0061] [Table 2]

[0062] At step 336, a user can view the results of the simulation (334) and post-processing (335). Viewing may be provided via an interactive environment within a cloud-based implementation that facilitates viewing and comparison of user data.

[0063] Method 330 can implement additional functionality at step 337 by first generating a reduced-size model. Specifically, at step 337, a 2D surface mesh (i.e., the mesh of the reduced-size model) is automatically generated using the engine surface mesh. The 2.5D FEM reduced-size model of the original engine (from step 331) is used at step 338 to evaluate different liner options and the effectiveness of the liners on noise reduction.

[0064] Embodiments can be used to evaluate any of a variety of turbofan engines known to those skilled in the art. Figures 4A-C show exemplary turbofan engine layouts that may be evaluated using embodiments. Layout 440a shown in Figure 4A is a bypass-only flow layout and does not include the core flow. Layout 440b shown in Figure 4B includes the bypass flow and the core flow. Note that in one embodiment, the combustion, high-pressure compressor (HPC), mixing and combustion chamber, and turbine sections of the core flow are not modeled because they are typically considered secondary for community noise evaluation and are very expensive in terms of computational resources. Layout 440c in Figure 4C is a variation of layout 440b in which the core flow merges with the bypass. Layout 440c represents the engine prototype used in testing.

[0065] 5A-D illustrate exemplary engine CAD geometries utilized in embodiments. For example, the CAD engine geometries illustrated in FIGS. 5A-D may be received at step 101 of method 100 described above in connection with FIG. 1, or similarly, at step 331 of method 330 described above in connection with FIG. 3. According to one embodiment, the CAD models illustrated in FIGS. 5A-D are prepared locally on the client side and then uploaded to a cloud platform implementing the embodiments.

[0066] FIG. 5B shows the nacelle, bypass, and core sections of a turbofan model 550. FIG. 5A is an enlarged view 552 of a core section portion 551 of the turbofan 550. In FIGS. 5A and 5B, each section of the engine is designated by a specific designation 553a-p, specifically, *PRIMARY*_LE* (553a), *PRIMARY*_inner* (553b), *PRIMARY*_outer* (553c), *NACELLE*_LE* (553d), *NACELLE*_TE* (553e), *BIFURCATION* (553f), *exhaust*plug* (553g), respectively. ), *exhaust*duct*(553h), *exhaust*duct*(553i), *PRIMARY*_TE*(553j), *BYPASS*_inner*(553k), *BYPASS*_outer*(553l), *NACELLE*_outer*(553m), *SPINNER*(553n), *outlet_BC*(553o), and *inlet_BC*(553p). Similarly, Figure 5C illustrates turbofan and outlet guide vane blades, with blade portions designated 561a-f according to a predetermined 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 illustrates a low pressure compressor (LPC) 570, with portion names designated 571a-c (IGVX[X]) and 572a-b (BOOSTERX[X]). For LPC 570, all stages are identified (named) with a different number X[X], with guide vane stage (stator) portions 571a-c named IGVX[X]* and rotor stage portions 572a-b named BOOSTERX[X]*. Additionally, blade 570 segments can also be identified using the nomenclature 561a-f used to name portions of blade 560: *_LE*, *_TE*, *_root*, *_TIP*, *_SS*, and *_PS*.

[0067] In Figures 5A-D, portions of a turbofan engine are named 553a-p, 561a-f, 571a-c, and 572a-b according to a preset naming scheme specified by computer code instructions implementing embodiments. In this manner, the computer code instructions implementing embodiments can identify specific portions of the model and determine characteristics of those portions for purposes of executing methods, e.g., to automatically create solver input files. In Figures 5A-D, asterisks (metacharacters) designate portions of surface names that can be optionally added / modified.

[0068] 6A-D, 7A-C, 8A-B, 9A-B, and 10A-B show example results obtained according to embodiments. According to one embodiment, the results shown in FIGS. 6A-D, 7A-C, 8A-B, 9A-B, and 10A-B are generated using respective post-processing tools, the names of which are provided below. In one embodiment, these tools are used to post-process the results from the embodiments described herein, for example, to process the simulation results from step 103.

[0069] Figures 6A-D show flow field visualizations 660a-d determined using the PowerVIZ tool. Different shading and / or color coding in Figures 6A-D is used to indicate the values ​​of the illustrated data. Figures 6A and 6B are contour images of surfaces showing flow variables. Specifically, Figure 6A illustrates a flow field 660a through a turbofan engine, and Figure 6B shows an expansion field 660b. Additionally, Figure 6C shows the processed results of a filtered pressure field 660c, which visually indicates the directionality of noise propagation for selected frequencies (typically the fan blade passing frequency and its multiples). Figure 6D is a visualization 660d of a surface mated to a body.

[0070] One embodiment generates radial and intersecting surfaces on which flow data can be displayed, for example, in stage 3. Intersecting surfaces show flow parameters on a plane perpendicular to the coordinate axes. Radial surfaces show flow parameters on a series of circular surfaces arranged at radial positions and can be used to analyze flow behavior through engine stages at different blade span positions (particular blade span positions can be specified by the user in stage 1).

[0071] Figures 7A-C show bypass / core flow phase-lock data obtained using the OptydB_phaselock tool. Different shading and / or color coding is used in Figures 7A-C to indicate the values ​​of the illustrated data. Figure 7A is a visualization 770a of the phase-locked mean fan wake velocity. Figure 7B is a visualization 770b of the root mean square (RMS) of the phase-locked mean fan wake velocity fluctuation, and Figure 7A presents a visualization 770c of the phase-locked mean blade pressure. Visualizations 770a-c show results for both the engine bypass and core sections, with flow variables time-averaged for the same fan angular position to provide an average wake flow image. This is available before and after each bladed stage (if present: fan, OGV, and low-pressure compressor (LPC) rotor / stator stages). Additionally, blade surface data can be processed in a similar manner, providing time-averaged 3D blade load contours and 2D plots collected at different blade span positions. This allows each blade load distribution to be analyzed individually.

[0072] 8A-B show noise production data obtained using the OptydB_footprint tool. FIG. 8A is a map 880 in which color / shading indicates effective perceived noise level (EPNL), with color / shading indicating EPNL according to legend 881. FIG. 8B is a plot 890 of perceived ground noise, comparing high-altitude flight 891 with high-altitude flight time 892. Plot 890 includes data for perceived noise level (PNL) 894 and PNLt (tone-corrected PNL) 893. According to one embodiment, the far-field sound perceived at 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 Farasat 1A formula). From this, an image can be generated showing 2D noise production (e.g., perceived ground noise in map 880) for a given flight maneuver (the default is approach, but a custom engine trajectory can be specified by the user). This can provide direct information about the operability of an engine at a given location, for example, when the generated noise map is overlaid on an actual airport satellite diagram at a similar scale. In one embodiment, noise levels are provided in terms of overall acoustic sound pressure level (OASPL), perceived noise level (PNL), and effective perceived noise level (EPNL).

[0073] 9A-B show noise directivity data obtained using the OptydB_directivity tool. FIG. 9A is a noise directivity polar map 990, with color / shading indicating noise directivity according to legend 991. FIG. 9B is a plot 993 of power watt level (i.e., power level) (dB / Hz) versus frequency (hz) 994. In one embodiment, the noise radiated by a turbofan engine is calculated and a polar directivity map, e.g., 990, is generated. The polar directivity map can highlight the specific angles from which the loudest noise components propagate, helping to identify the areas of the engine that cause the most noise. Contour plots can also be provided in terms of sound pressure level (SPL) in two different bands, 3 / 8 and 1 / 12 octave bands. Additionally, engine power watt levels (PWL) can be provided to quickly compare different designs or flow conditions and rank them from quietest to loudest.

[0074] 10A-B show intake / outlet duct mode analysis data obtained using the OptydB_azi tool, according to one embodiment. FIG. 10A is a graph 1010 showing fan intake cut-on and cut-off modes in terms of frequency 1011 compared to azimuthal mode number 1012, with color / shading provided according to legend 1013. FIG. 10B is a fan intake azimuthal model shape plot 1014 in terms of sound pressure, with color / shading according to legend 1015 indicating sound pressure. In one embodiment, the pressure fields collected on the fan intake and OGV outlet surfaces are projected onto their azimuthal and radial modes in the frequency domain to obtain a database used to analyze nacelle acoustics in the presence of a liner. This projected dataset contour can then be used to generate plots, such as 1010, showing engine cut-off / cut-off mode ranges. These allow users to quantitatively analyze the interaction between the fan wake and the OGV.

[0075] 11 illustrates a method 1100 of noise reduction analysis, according to one embodiment. According to one embodiment, method 1100 is a reduced method in that method 1100 is reduced in size (e.g., mesh size) and number of equations solved. This reduces both memory and computational resources required. In such an embodiment, method 1100 is a simplification of methods 100 and 330, and although method 1100 does not produce the same amount of detailed results as the original methods, e.g., 100 and 330, it can provide a basic assessment of important advantages for driving design iterations.

[0076] Method 1100 begins in step 1101 by obtaining an engine shape 1120. This engine shape 1120 is used in step 1103 to perform a 3D simulation and generate simulation results, e.g., plot 1121. Further, engine shape 1120 is meshed in step 1102 to create a 2D mesh 1122 of the turbofan engine. In step 1104, the results of the simulation performed in step 1103 are further processed to determine fan ingestion characteristics, e.g., in the form of plot 1123. Method 1100 continues by performing a 2.5D FEM acoustic analysis in step 1105 using (i) the 2D mesh 1122 and (ii) the processed results of the fan ingestion data, e.g., 1123. The results of the acoustic analysis in step 1105 may be plotted, e.g., in plot 1124. Finally, in step 1106, the far-field noise is determined. The determined far-field sound may be plotted and provided to the user, for example as shown in plot 1125.

[0077] In one embodiment, method 1100 can prepare a 2.5D FEM reduced model for liner noise reduction evaluation. According to one embodiment, duct mode analysis can be performed on the results of 3D simulation 1103 to express the pressure field in terms of azimuthal modes in the frequency domain. This pressure field data can be used to directly configure a reduced 2.5D model of the engine for further analysis. The simulation can generate 2D pressure wave contours for specified frequencies. Several frequencies can be analyzed through multiple runs, and the results can be collected and combined to generate a far-field power watt level (PWL) curve, such as plot 1125. Furthermore, in an embodiment, a user can submit processes for different liner characteristics or spatial extensions. This allows a user to quickly make tradeoffs to identify the configuration most likely to produce the greatest noise reduction for a given frequency range of interest.

[0078] One embodiment provides a fully automated process for area noise assessment of turbofan engines, along with a scheme of the supporting infrastructure used to execute the process. In one embodiment, the process is divided into three stages: 1) generating a digital twin of the actual engine, 2) performing a 3D high-fidelity CFD analysis, and 3) processing the simulation results. According to one embodiment, the proposed methodology and infrastructure enable fully automated execution of the three aforementioned stages. The embodiment can simultaneously run multiple simulations. Each simulation may include modified engine geometries or the same engine for multiple operating conditions. Once the 3D simulation is complete, the results can be automatically processed to generate a readable dataset that is interactively accessible within the system. This allows users to quickly assess the noise generated by a given engine as well as identify possible design improvements by, for example, performing trade-off studies. Furthermore, a smaller 2D model can be generated from a vertical cut of the original engine geometry to generate a reduced-scale model for nacelle aeroacoustic analysis. This reduced-scale model can be used to quickly analyze possible noise reductions that can be achieved with passive noise reduction devices, such as liners installed inside the nacelle. Advantages of such an approach include the ability to run shorter simulations and therefore analyze a large number of liner configurations and impedance values ​​in a very short time that would not be feasible with physical testing.

[0079] One embodiment provides a computer-implemented automated methodology for performing flow simulation and area noise assessment of a turbofan engine within a PaaS environment. Such an embodiment imports a file containing a digitized representation of a three-dimensional engine geometry, automatically identifies different engine components, and generates a digital twin that can be simulated using CFD software. The embodiment performs a high-fidelity CFD simulation to calculate the aerodynamic and far-field acoustic characteristics of the turbofan engine. A data set is generated by processing the results of the CFD simulation. The data indicates key design advantages and disadvantages of the model and pinpoints areas to be modified or redesigned. These identified problem areas can be redesigned, and an improved real-world turbofan can then be manufactured.

[0080] Embodiments may also generate a 2D reduced model of the original 3D engine to perform a trade-off analysis on the benefits of a liner attached to the engine nacelle in terms of noise levels generated in the far field.

[0081] One embodiment creates a digital twin by reading the original CAD engine geometry, generating a surface mesh, and calculating the size and position of several engine parts along with characteristic geometric parameters such as fan diameter, number of blades, and fan tip gap size. Such an embodiment generates, from scratch, support entities used to define the computational volume mesh local size, measurement surfaces, etc. User-specified input parameters are then imported, and the solver pre-processing software is managed by assigning input parameters and invoking and generating solver input files.

[0082] Another exemplary embodiment performs CFD analysis using a scheduling system to submit single or multiple runs on a remote or local cluster, and centralized storage to store data generated by the 3D engine simulation for subsequent post-processing.

[0083] In one embodiment, performing post-processing involves running a series of post-processing tools to generate comprehensive acoustic and aerodynamic data sets for each previously run 3D simulation, and providing interactive access via a single interface to all data sets in an easily viewable environment where users can analyze simulation results and compare different runs.

[0084] In yet another embodiment, generating a 2D reduced model and performing a trade-off analysis includes extracting a vertical 2D cut of the original engine 3D geometry and generating a 2D mesh used for the reduced model analysis. This embodiment extracts a projected pressure field on the fan intake section and OGV exit section from the processed 3D engine dataset, then reads the simulation parameter list, the projected pressure field, and the user-specified liner impedance, and generates a reduced 2.5D FEM model for aeroacoustic analysis of the nacelle. To proceed, a series of single-frequency calculations of the reduced model can be performed to cover a given frequency range of interest specified by the user. The generated noise is collected with a series of microphones placed in the far field via the FW-H method, and the generated data is collected and evaluated to determine the far-field sound level.

[0085] Examples of benefits

[0086] Embodiments provide the ability to automatically perform the simulation and noise evaluation process. Such functionality may be provided by the tools selected to implement this process and from the proposed methodology presented herein on how to combine different data sets. In contrast, existing methods require users 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., sampling rate based on frequencies of interest), and manually create a series of stages and surfaces to determine the flow and measure flow characteristics.

[0087] Embodiments also provide a reduction in the cost and time required to obtain accurate engine noise predictions, which can identify design flaws. Additionally, embodiments can be used to evaluate noise reduction achievable by installing passive noise reduction devices (liners).

[0088] Being able to identify design flaws early in turbofan engine design can avoid spending a lot of time on expensive physical testing and reduce the risk of late-stage corrections. Furthermore, early assessment of engine aeronautical performance and estimation of its noise production can lead to more efficient engines and better aircraft designs with maximum maneuverability. The implementation of noise reduction devices such as liners can further extend engine maneuverability without limiting its performance (thrust). Due to the large number of liner design space parameters, reduced models are very useful because they can be used to quickly iterate through multiple solutions.

[0089] Embodiments can be used to digitally determine the characteristics of a real-world turbofan, for example, for compliance purposes. Additionally, embodiments can be used to determine an optimized turbofan design, which can then be manufactured for real-world use. To illustrate, one embodiment can determine design flaws in a turbofan, and in response to the identified flaws, the turbofan can be redesigned and manufactured for real-world use.

[0090] Additionally, embodiments can be used to improve existing turbofans. For example, it can be determined that an existing turbofan does not meet noise regulations, and then a CAD model of the turbofan can be created (e.g., by measuring the real-world turbofan), which can be used in embodiments to determine design changes or implementation improvements, such as an improved liner, that can be used in the real world to bring the turbofan into compliance.

[0091] Computer Support

[0092] FIG. 12 is a simplified block diagram of a computer-based system 1220 that can be used to implement any of the various embodiments of the invention described herein. System 1220 includes a bus 1223. Bus 1223 serves as an interconnect between the various components of system 1220. Attached to bus 1223 is an input / output device interface 1226 for connecting various input and output devices, such as a keyboard, mouse, display, and speakers, to system 1220. A central processing unit (CPU) 1222 is attached to bus 1223 and provides for the execution of computer instructions implementing embodiments, such as methods 100, 330, 1100, and system 220. Memory 1225 provides volatile storage for data used to execute computer instructions implementing embodiments described herein, such as those previously described herein. 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 a variety of networks known in the art, including wide area networks (WANs) and local area networks (LANs).

[0093] It should be understood that the exemplary embodiments described herein may be implemented in many different ways. In some instances, the various methods and systems described herein may each be implemented by a physical, virtual, or hybrid general-purpose computer, such as computer system 1220 or a computer network environment, such as computer environment 1330 described herein below in connection with FIG. 13. Computer system 1220 may be converted into a system that executes the methods described herein (e.g., 100, 330, 1100), for example, by loading software instructions into either memory 1225 or non-volatile storage 1224 for execution by CPU 1222. Those skilled in the art should further appreciate that system 1220 and its various components may be configured to implement any embodiment or combination of embodiments described herein. Furthermore, system 1220 may implement the various embodiments described herein utilizing any combination of hardware, software, and firmware modules operably coupled internally or externally to system 1220.

[0094] 13 illustrates a computer network environment 1330 in which embodiments can be implemented. In the computer network environment 1330, a server 1331 is linked to clients 1333a-n through a communication network 1332. The environment 1330 can be used to enable the clients 1333a-n, alone or in combination with the server 1331, to perform any of the embodiments described herein. As non-limiting examples, the computer network environment 1330 can provide cloud computing embodiments, software as a service (SAAS) embodiments, and the like.

[0095] 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 configured to enable a processor to load the software, or a subset of its instructions. The processor is then configured to execute the instructions to operate a device or cause it to operate in a method described herein.

[0096] Furthermore, firmware, software, routines, or instructions may be described herein as performing certain operations and / or functions of a data processor, although it will be understood that such descriptions contained herein are merely for convenience and that such actions actually result from a computing device, processor, controller, or other device executing the firmware, software, routines, instructions, etc.

[0097] It should be understood that the flow diagrams, block diagrams, and network diagrams may include more or fewer elements, may be arranged differently, or may be represented differently, but it should also be understood that a particular implementation may define the block diagrams and network diagrams, and some block diagrams and network diagrams illustrating the implementation of embodiments, in a particular way.

[0098] Accordingly, further embodiments may also be implemented in various computer architectures, physical computers, virtual computers, cloud computers, and / or some combination thereof, and therefore the data processors described herein are intended to be illustrative only and not limiting of the embodiments.

[0099] The teachings of all patents, published applications, and references cited herein are incorporated by reference in their entirety.

[0100] While exemplary embodiments have been particularly shown and described, those skilled in the art will understand that various changes in form and details can be made therein without departing from the scope of the embodiments encompassed by the appended claims.

[0101] For example, the foregoing description and details of the illustrated embodiments refer to Applicant-Assignee (Dassault Systemes Americas Corporation) and Dassault Systemes tools and platforms for purposes of illustration, but not limitation. Other similar tools and platforms are suitable.

Claims

1. 1. A computer-implemented method for determining physical characteristics of a turbofan engine, the method comprising: acquiring, in a memory coupled to the processor, (i) a computer-aided design (CAD) model representative of the turbofan engine, and (ii) a representation of flow conditions; automatically determining a solver input file based on the CAD model and the representation of the flow conditions; responsively performing a simulation of the turbofan engine subjected to the flow conditions using the determined solver input file, wherein results of the simulation are indicative of physical characteristics of the turbofan engine.

2. The method of claim 1 , wherein the processor is one of a plurality of processors supporting a global network platform service.

3. The method of claim 2 , wherein the CAD model and representation of flow conditions are obtained in response to user input via a user interface of the platform service.

4. The method of claim 1 , wherein the flow conditions include at least one of freestream airspeed, fan rotational speed, and air temperature.

5. The method of claim 1 , wherein the determined solver input file includes at least one of a surface mesh, a measurement surface, and a representation of the flow state.

6. determining the solver input file; creating the surface mesh; and creating the measurement surface within the surface mesh.

7. generating the surface mesh, analyzing the CAD model to determine at least one geometric parameter of engine bounding box dimensions, nacelle leading and trailing edge location and shape, fan diameter, fan blade count, fan tip gap size, fan blade leading and trailing edge shape, outlet guide vane location, outlet guide vane leading and trailing edge shape, low pressure compressor stage location, low pressure compressor stage blade count, low pressure compressor stage leading and trailing edge shape, and low pressure compressor rotor stage fan tip gap size; and identifying the turbofan engine part based on the names of each of the components of the CAD model representing the identified part.

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

9. conducting the simulation, generating a volume mesh based on the surface mesh; and performing the simulation using the generated volumetric mesh.

10. setting a local mesh resolution for at least a portion of the volumetric mesh; and setting a volumetric mesh resolution region based on the size and shape of elements in the surface mesh.

11. conducting the simulation, The method of claim 5 , further comprising determining simulation conditions.

12. determining the simulation conditions, determining a position of the measurement surface based on the representation of flow conditions and the size and shape of elements in the surface mesh; setting the length of the simulation based on a minimum frequency of interest; and setting the sampling rate based on a maximum frequency of interest.

13. The method of 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 of claim 1 , wherein the determined physical properties include at least one of aerodynamic properties, thermal properties, and acoustic properties.

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

16. 1. A system for determining physical characteristics of a turbofan engine, comprising: a processor; and a memory having computer code instructions stored thereon, wherein the processor and memory use the computer code instructions to cause the system to: obtaining, in said memory, (i) a computer-aided design (CAD) model representative of a turbofan engine, and (ii) a representation of flow conditions; automatically determining a solver input file based on the CAD model and the representation of the flow conditions; and responsively performing a simulation of the turbofan engine subjected to the flow conditions using the determined solver input file, wherein results of the simulation are indicative of physical characteristics of the turbofan engine.

17. The system of claim 16 , wherein the determined solver input file includes at least one of a surface mesh, a measurement surface, and a representation of the flow conditions.

18. In determining the solver input file, the processor and the memory use the computer code instructions to instruct the system to: creating the surface mesh; and creating the measurement surface within the surface mesh.

19. 1. A system for determining physical characteristics of a turbofan engine, comprising: a processor; and a memory having computer code instructions stored thereon, wherein the processor and memory use the computer code instructions to cause the system to: obtaining, in a platform memory, (i) a computer-aided design (CAD) model representative of the turbofan engine, and (ii) a representation of flow conditions; determining a solver input file based on the CAD model and the representation of flow conditions; and running a simulation of the turbofan engine subjected to the flow conditions using the determined solver input files, wherein results of the simulation are indicative of physical characteristics of the turbofan engine.

20. 1. A computer program product for determining physical characteristics of a turbofan engine, comprising: one or more non-transitory computer-readable storage devices; and program instructions stored in at least one of the one or more storage devices, the program instructions, when read and executed by a processor, causing a device associated with the processor to: Obtaining (i) a computer-aided design (CAD) model representative of a turbofan engine, and (ii) a representation of flow conditions; determining a solver input file based on the CAD model and the representation of flow conditions; and performing a simulation of the turbofan engine subjected to the flow conditions using the determined solver input file, wherein results of the simulation are indicative of physical characteristics of the turbofan engine.

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