CN116187034A - Uncertainty quantification-based compressor simulation credibility assessment method - Google Patents
Uncertainty quantification-based compressor simulation credibility assessment method Download PDFInfo
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Abstract
The present disclosure provides a compressor simulation reliability assessment method based on uncertainty quantization to improve assessment accuracy. The method comprises the following steps: obtaining uncertainty of overall performance parameters based on node distribution information of the discrete network of the object to be evaluated; evaluating according to the consistency of the simulation result and the experimental result to obtain uncertainty of a parameter distribution curve, wherein the parameter distribution curve comprises a distribution curve of static pressure parameters and/or Mach number parameters; and obtaining the credibility of the object to be evaluated according to the uncertainty of the overall performance parameters and the uncertainty of the parameter distribution curve. By implementing the technical scheme disclosed by the invention, the accuracy of reliability evaluation can be improved.
Description
Technical Field
The invention relates to the field of aviation, in particular to a compressor simulation credibility assessment method based on uncertainty quantification.
Background
The gas compressor is a main component of an aeroengine, the geometrical and physical models involved in the gas compressor pneumatic simulation are very complex, a strong coupling nonlinear partial differential equation is required to be calculated in a numerical solution mode, in the process, factors influencing the system reliability comprise various factors such as geometrical dimensions, discrete formats, numerical formats, boundary conditions, grid scales and the like besides the physical models, indexes are difficult to be independent, and an index system similar to the system simulation cannot be established for layered verification. In addition, the complexity of the aeroengine itself creates a high degree of complexity in its internal flow phenomena, which places more comprehensive and stringent demands on the pneumatic simulation software. In order to promote the application of the pneumatic simulation technology in the engine development process, the reliability assessment problem of pneumatic simulation software needs to be solved. The uncertainty is defined in the field of system simulation as a potential defect occurring in the modeling process due to lack of knowledge, and can be quantitatively considered as an index parameter describing the error dispersion degree of the target parameter. Currently, the reliability evaluation work of numerical simulation still stays at a simple error calculation level, and the simulation result cannot be comprehensively and effectively evaluated. Therefore, the reliability evaluation theory in other fields needs to be explored or referred to, the reliability evaluation of numerical simulation is supported, and the technical problem of lower accuracy of the existing evaluation method is solved.
Disclosure of Invention
In order to solve at least one technical problem in the prior art, the present disclosure provides a compressor simulation reliability evaluation method based on uncertainty quantization, so as to improve evaluation accuracy.
According to a first aspect of the present disclosure, there is provided a compressor simulation reliability assessment method based on uncertainty quantization, including:
obtaining uncertainty of overall performance parameters based on node distribution information of the discrete network of the object to be evaluated;
evaluating according to the consistency of the simulation result and the experimental result to obtain uncertainty of a parameter distribution curve, wherein the parameter distribution curve comprises a distribution curve of static pressure parameters and/or Mach number parameters;
and obtaining the credibility of the object to be evaluated according to the uncertainty of the overall performance parameter and the uncertainty of the parameter distribution curve.
Optionally, the obtaining the uncertainty of the overall performance parameter based on node distribution information of the discrete network of the object to be evaluated includes:
based on node distribution information of the discrete network of the object to be evaluated, obtaining an accurate solution value of the overall performance parameter by a method of Legensen extrapolation;
obtaining a discrete error according to the difference value between the accurate estimated value and the simulation result;
and obtaining uncertainty of the discrete error as uncertainty of the overall performance parameter according to the discrete error.
Optionally, based on node distribution information of the discrete network of the object to be evaluated, obtaining an accurate solution value of the overall performance parameter by a method of richardson extrapolation, including:
obtaining the size factor of the discrete grid according to node distribution information of the flow direction, the spreading direction and the B2B direction in the process of dividing the discrete network of the object to be evaluated;
generating three sets of discrete networks with geometrical similarity according to the discrete network of the object to be evaluated;
determining encryption factors among the sets of discrete networks;
and obtaining an accurate solution value of the overall performance parameter by a method of Lechadson extrapolation.
Optionally, the accurate solution value of the overall performance parameter is obtained according to the following formula:
wherein ,
representing an accurate solution to the overall performance parameter, f h Representing the overall performance parameter calculation, f rh And representing the calculated values of the overall performance parameters under different grid scales, wherein r represents the precision grid encryption factor, and p represents the observation precision order.
Optionally, the uncertainty of the overall performance parameter is obtained according to the following formula:
wherein u representsUncertainty of numerical error, r represents precision grid encryption factor, p represents observation precision order, f 2 Representing a numerical solution under a second set of grid parameters, f 1 Representing a numerical solution under a first set of grid parameters.
Optionally, the following formula is used for evaluation according to the consistency of the simulation result and the experimental result:
wherein ρ (X, Y) represents a consistency metric, N represents the number of samples, X i Representing x sample values, y i Representing y sample values.
Optionally, the obtaining the credibility of the object to be evaluated according to the uncertainty of the overall performance parameter and the uncertainty of the parameter distribution curve includes:
and obtaining the credibility of the object to be evaluated according to the uncertainty and the importance degree.
In a second aspect of the present disclosure, there is provided a compressor simulation reliability evaluation apparatus based on uncertainty quantization, including:
the first module is used for obtaining uncertainty of overall performance parameters based on node distribution information of the discrete network of the object to be evaluated;
the second module is used for evaluating according to the consistency of the simulation result and the experimental result to obtain the uncertainty of a parameter distribution curve, wherein the parameter distribution curve comprises a distribution curve of static pressure parameters and/or Mach number parameters;
and the third module is used for obtaining the credibility of the object to be evaluated according to the uncertainty of the overall performance parameter and the uncertainty of the parameter distribution curve.
In a third aspect of the present disclosure, there is provided an electronic apparatus, including:
a processor; and
a memory in which a program is stored,
wherein the program comprises instructions which, when executed by the processor, cause the processor to perform the method according to any of the first aspects of the present disclosure.
In a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method according to any one of the first aspects of the present disclosure.
By the aid of the one or more technical schemes, the technical effect of improving reliability evaluation accuracy can be achieved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the disclosure and together with the description serve to explain the principles of the disclosure.
FIG. 1 illustrates a flow chart of a method of compressor simulation reliability assessment based on uncertainty quantization in accordance with an exemplary embodiment of the present disclosure;
FIG. 2 illustrates an error band distribution plot of grid dispersion errors according to an exemplary embodiment of the present disclosure;
fig. 3 shows a graph comparing simulation results with experimental results according to an exemplary embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure have been shown in the accompanying drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "including" and variations thereof as used herein are intended to be open-ended, i.e., including, but not limited to. The term "based on" is based at least in part on. The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments. Related definitions of other terms will be given in the description below. It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
The aspects of the present disclosure are described below with reference to the accompanying drawings:
referring to fig. 1, a compressor simulation reliability assessment method based on uncertainty quantization includes:
s101, obtaining uncertainty of overall performance parameters based on node distribution information of the discrete network of the object to be evaluated.
In the step, based on node distribution information of a discrete network of an object to be evaluated, an accurate solution value of the overall performance parameter can be obtained through a Lechadson extrapolation method; obtaining a discrete error according to the difference value between the accurate estimated value and the simulation result; from the discrete errors, uncertainty in the discrete errors is obtained as uncertainty in the overall performance parameter, i.e., error.
When the accurate solution value of the overall performance parameter is obtained by a method of Lechadson extrapolation according to node distribution information based on the discrete network of the object to be evaluated, the size factor of the discrete grid can be obtained according to node distribution information of three directions (defined as I, J and K directions) of flow direction, spanwise direction and B2B in the dividing process of the discrete network of the object to be evaluated; generating three sets of discrete networks with geometrical similarity according to the discrete network of the object to be evaluated; determining encryption factors among the sets of discrete networks; and obtaining an accurate solution value of the overall performance parameter by a method of Lechadson extrapolation.
The accurate solution value of the overall performance parameter is obtained according to the following formula:
wherein ,
representing an accurate solution to the overall performance parameter, f h Representing the overall performance parameter calculation, f rh And representing the calculated values of the overall performance parameters under different grid scales, wherein r represents the precision grid encryption factor, and p represents the observation precision order.
The uncertainty of the overall performance parameter is obtained according to the following formula:
wherein u represents uncertainty of numerical error, r represents precision grid encryption factor, p represents observation precision order, f 2 Representing a numerical solution under a second set of grid parameters, f 1 Representing a numerical solution under a first set of grid parameters.
Illustratively, the size factor h of the initial grid size is obtained according to node distribution information in three directions (defined as I, J and K directions) of flow direction, spanwise direction and B2B in the impeller mechanical numerical simulation structured grid division process i The method comprises the steps of carrying out a first treatment on the surface of the Generating three sets of grids with different scales on the basis of the initial grid to obtain a target aerodynamic performance parameter f i Encryption of the mesh is obtainedFactor r=h coarse /h fine ，h coarse Represents the grid scale of the coarse grid, h fine Representing the fine mesh grid scale. Wherein, the results of the three sets of grid overall performance parameters are shown in the table one.
List one
| Grid mesh | Flow (kg/s) | Total pressure ratio | Efficiency of |
| 1 | 20.17 | 1.979 | 0.834 |
| 2 | 20.24 | 1.982 | 0.832 |
| 3 | 20.28 | 1.984 | 0.831 |
For the three sets of grids, an observation accuracy order under the numerical algorithm is obtained by using a grid convergence index method, after the order is determined, an extrapolation solution is obtained by a rational Charles extrapolation method, a difference value between the extrapolation solution and a simulation result is the error of the simulation, and finally GCI (grid convergence index) is calculated to obtain uncertainty of the error, and related data such as an exemplary uncertainty solution are shown in a table II:
watch II
| Total pressure ratio | Efficiency of | |
| Convergence order/p | 2.0623 | 3.6757 |
| Accurate solution estimation/fexact | 1.847548 | 0.849094 |
| h1 numerical error/d 1 | 2.27E-3 | 1.97E-4 |
| h2 numerical error/d 2 | 3.90E-3 | 5.16E-4 |
| h3 numerical error/d 3 | 6.71E-3 | 1.35E-3 |
| Convergence index/GCI | 2.86E-2 | 5.60E-3 |
| Uncertainty/um | 1.43E-2 | 2.80E-3 |
S102, evaluating according to the consistency of the simulation result and the experimental result to obtain the uncertainty of the parameter distribution curve, wherein the parameter distribution curve comprises the distribution curve of the static pressure parameter and/or the Mach number parameter.
In the step, the consistency of the simulation result and the experimental result can be evaluated based on a Theil inequality method and a gray correlation method according to the parameter distribution curves such as static pressure, mach number and the like in the simulation result.
Specifically, the following formula can be used for evaluation according to the consistency of the simulation result and the experimental result:
wherein ρ (X, Y) represents a consistency metric, N represents the number of samples, X i Representing x sample values, y i Representing y sample values.
And S103, obtaining the credibility of the object to be evaluated according to the uncertainty of the overall performance parameters and the uncertainty of the parameter distribution curve.
In the step, based on engineering practice, the preliminary credibility of the object to be evaluated can be obtained according to the credibility and the importance degree of each parameter. And obtaining the credibility of the object to be evaluated according to the uncertainty and the importance degree, and judging whether the error requirement is met.
In the technical scheme, the grid convergence index, the grid size factors and the encryption factors among the grids are determined by generating the grid cell numbers in the streamline direction, the blade spanwise direction and the B2B direction which are commonly used in the structured grids by the impeller machinery grids, and the grid convergence index, the encryption factors among the grids can be approximately determined by the cube root of the total number of the grid cells in the unstructured grids so as to ensure the geometric similarity among the grids.
In the above technical solution, an accurate solution value of the overall performance parameter is obtained by a method of richardson extrapolation, and the accurate solution value is generally a p+1 order accurate solution value of a mathematical model. As the grid refines, the estimate converges to the exact solution faster than the numerical solution. Specifically, the calculation can be performed by using the formula (1).
In the above technical solution, the absolute or relative discrete error derived from the grid can be obtained by the difference between the above accurate solution value and the simulation result f. The uncertainty of the discrete error is calculated by the formula (2):
in the technical scheme, the consistency evaluation of the simulation result parameter distribution curve and the experimental measurement curve can be calculated through a formula (3):
in the above technical solution, the consistency evaluation method needs to display output curves of the simulation system and the real system. The simulation system and the real system are input in the same way, and corresponding output data are stored. By observing the outline of the curve, key points are set in places with severe output behavior change or other places needing important attention, then the time sequence is divided into corresponding time segments according to the key points, and meanwhile, the expert sequentially gives the threshold value corresponding to each time segment. According to different conditions, the coefficients of the fragments are calculated respectively by using the formulas. And comparing the obtained coefficients with the thresholds corresponding to the time slices respectively, if the coefficients are smaller than the thresholds corresponding to the time slices, the difference between the simulation model output X and the real system output Y can be considered to be insignificant, the simulation model is effective, otherwise, the significant difference exists between the simulation model output X and the real system output Y, the simulation result is incorrect, and the simulation model is unreliable. In engineering, 0.3 is generally used as a threshold value for judging whether the actual measurement sample is consistent with the simulation result thereof, so that the actual measurement sample and the simulation result thereof can be considered to have obvious difference, and the simulation result is incorrect.
Through the above processing, the reliability evaluation of the overall performance parameters and parameter distribution curves of the pneumatic simulation of the compressor is realized.
In summary, the beneficial effects of the invention are as follows:
the invention utilizes an uncertainty quantization method and combines the pneumatic simulation characteristics of the air compressor to provide a grid discrete error and uncertainty calculation method based on a structured grid, and simultaneously provides an air compressor parameter distribution curve evaluation means based on curve consistency evaluation to realize the reliability evaluation of one-dimensional overall performance parameters and two-dimensional parameter distribution of the pneumatic simulation of the air compressor.
Illustratively, a grid discrete error band distribution based on a structured grid and a grid convergence index is shown in FIG. 2, where the error band represents the interval within which the overall performance parameter falls at a confidence level of 95%;
exemplary graph pairs based on a consistency assessment method are shown in fig. 3, where the calculation coefficients are as follows:
watch III
| Sequence number | Reference time | Reference data | Simulation time | Simulation data |
| 1 | 1.000000 | 101206.5 | 1.000000 | 99439.72 |
| 2 | 2.000000 | 100901.9 | 2.000000 | 101009.0 |
| 3 | 3.000000 | 100680.0 | 3.000000 | 101768.6 |
| 4 | 4.000000 | 100543.3 | 4.000000 | 101402.4 |
| 5 | 5.000000 | 100483.8 | 5.000000 | 101135.7 |
| 6 | 6.000000 | 100495.6 | 6.000000 | 101576.8 |
| 7 | 7.000000 | 100573.2 | 7.000000 | 102256.0 |
| 8 | 8.000000 | 100710.8 | 8.000000 | 102767.6 |
| 9 | 9.000000 | 100902.7 | 9.000000 | 103064.2 |
| 10 | 10.000000 | 101143.3 | 10.000000 | 103227.6 |
| 11 | 11.000000 | 101426.8 | 11.000000 | 103353.5 |
| 12 | 12.000000 | 101747.5 | 12.000000 | 103509.6 |
Wherein, the TIC coefficient is 0.004, and the parameter consistency is 97.572.
According to the technical scheme, aiming at the problems of insufficient reliability of the pneumatic simulation result of the air compressor, missing simulation error evaluation means and the like, grid discrete errors and uncertainty of the overall performance parameters of the air compressor can be calculated through node distribution information of multiple sets of structured grids in three directions of flow direction, direction of expansion and B2B based on a grid convergence factor method, and error distribution of the overall performance parameters is obtained. And obtaining the differences of the static pressure, mach number and other parameter distribution curves and experimental results in curve shapes and values by a consistency evaluation method. And carrying out preliminary evaluation on the reliability of the result to be evaluated according to the quantized result, and improving the reliability of the simulation result.
In one embodiment, the present disclosure further provides a compressor simulation reliability assessment apparatus based on uncertainty quantization, including:
the first module is used for obtaining uncertainty of the overall performance parameter based on node distribution information of the discrete network of the object to be evaluated.
And the second module is used for evaluating according to the consistency of the simulation result and the experimental result to obtain the uncertainty of the parameter distribution curve, wherein the parameter distribution curve comprises the distribution curve of the static pressure parameter and/or the Mach number parameter.
And the third module is used for obtaining the credibility of the object to be evaluated according to the uncertainty of the overall performance parameter and the uncertainty of the parameter distribution curve. The specific implementation manner and corresponding effect of the device can refer to the content of the compressor simulation credibility assessment method based on uncertainty quantification, and the description is not repeated here.
The exemplary embodiments of the present disclosure also provide an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor. The memory stores a computer program executable by the at least one processor for causing the electronic device to perform a method according to embodiments of the present disclosure when executed by the at least one processor.
The present disclosure also provides a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is for causing the computer to perform a method according to an embodiment of the present disclosure.
The present disclosure also provides a computer program product comprising a computer program, wherein the computer program, when executed by a processor of a computer, is for causing the computer to perform a method according to embodiments of the disclosure.
A structural block diagram of an electronic device that may be a server or a client of the present disclosure, which is an example of a hardware device that may be applied to aspects of the present disclosure, will now be described. Electronic devices are intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
The electronic device includes a computing unit that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) or a computer program loaded from a storage unit into a Random Access Memory (RAM). In the RAM, various programs and data required for the operation of the device may also be stored. The computing unit, ROM and RAM are connected to each other by a bus. An input/output (I/O) interface is also connected to the bus.
A plurality of components in an electronic device are connected to an I/O interface, comprising: an input unit, an output unit, a storage unit, and a communication unit. The input unit may be any type of device capable of inputting information to the electronic device, and may receive input numeric or character information and generate key signal inputs related to user settings and/or function controls of the electronic device. The output unit may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, video/audio output terminals, vibrators, and/or printers. Storage units may include, but are not limited to, magnetic disks, optical disks. The communication unit allows the electronic device to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and/or chipsets, such as bluetooth (TM) devices, wiFi devices, wiMax devices, cellular communication devices, and/or the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
As used in this disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
Claims (10)
1. The compressor simulation credibility assessment method based on uncertainty quantification is characterized by comprising the following steps of:
obtaining uncertainty of overall performance parameters based on node distribution information of the discrete network of the object to be evaluated;
evaluating according to the consistency of the simulation result and the experimental result to obtain uncertainty of a parameter distribution curve, wherein the parameter distribution curve comprises a distribution curve of static pressure parameters and/or Mach number parameters;
and obtaining the credibility of the object to be evaluated according to the uncertainty of the overall performance parameter and the uncertainty of the parameter distribution curve.
2. The method according to claim 1, wherein the obtaining uncertainty of the overall performance parameter based on node distribution information of the discrete network of objects to be evaluated comprises:
based on node distribution information of the discrete network of the object to be evaluated, obtaining an accurate solution value of the overall performance parameter by a method of Legensen extrapolation;
obtaining a discrete error according to the difference value between the accurate estimated value and the simulation result;
and obtaining uncertainty of the discrete error as uncertainty of the overall performance parameter according to the discrete error.
3. The method according to claim 2, wherein obtaining the accurate solution value of the overall performance parameter by a richardson extrapolation method based on node distribution information of the discrete network of objects to be evaluated comprises:
obtaining the size factor of the discrete grid according to node distribution information of the flow direction, the spreading direction and the B2B direction in the process of dividing the discrete network of the object to be evaluated;
generating three sets of discrete networks with geometrical similarity according to the discrete network of the object to be evaluated;
determining encryption factors among the sets of discrete networks;
and obtaining an accurate solution value of the overall performance parameter by a method of Lechadson extrapolation.
4. A method according to claim 3, wherein the accurate estimate of the overall performance parameter is obtained according to the following formula:
wherein ,
representing an accurate solution to the overall performance parameter, f h Representing the totalityCalculated value of energy parameter f rh And representing the calculated values of the overall performance parameters under different grid scales, wherein r represents the precision grid encryption factor, and p represents the observation precision order.
5. The method of claim 2, wherein the uncertainty of the overall performance parameter is derived from the formula:
wherein u represents uncertainty of numerical error, r represents precision grid encryption factor, p represents observation precision order, f 2 Representing a numerical solution under a second set of grid parameters, f 1 Representing a numerical solution under a first set of grid parameters.
6. The method of claim 1, wherein the evaluation is based on consistency of simulation results with experimental results using the formula:
wherein ρ (X, Y) represents a consistency metric, N represents the number of samples, X i Representing x sample values, y i Representing y sample values.
7. The method according to claim 1, wherein said deriving the confidence level of the object under evaluation from the uncertainty of the overall performance parameter and the uncertainty of the parameter profile comprises:
and obtaining the credibility of the object to be evaluated according to the uncertainty and the importance degree.
8. An uncertainty quantization-based compressor simulation reliability evaluation device, comprising:
the first module is used for obtaining uncertainty of overall performance parameters based on node distribution information of the discrete network of the object to be evaluated;
the second module is used for evaluating according to the consistency of the simulation result and the experimental result to obtain the uncertainty of a parameter distribution curve, wherein the parameter distribution curve comprises a distribution curve of static pressure parameters and/or Mach number parameters;
and the third module is used for obtaining the credibility of the object to be evaluated according to the uncertainty of the overall performance parameter and the uncertainty of the parameter distribution curve.
9. An electronic device, comprising:
a processor; and
a memory in which a program is stored,
wherein the program comprises instructions which, when executed by the processor, cause the processor to perform the method according to any of claims 1-7.
10. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-7.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107231408A (en) * | 2017-05-03 | 2017-10-03 | 同济大学 | The building method for the connectivity modeling predicted in urban road based on vehicle-state |
| CN109190901A (en) * | 2018-08-03 | 2019-01-11 | 中国船舶工业综合技术经济研究院 | The credible evaluation method of reliability assessment result based on multi objective measurement |
| US10366362B1 (en) * | 2012-10-18 | 2019-07-30 | Featuremetrics, LLC | Feature based modeling for forecasting and optimization |
| CN110889207A (en) * | 2019-11-08 | 2020-03-17 | 江南机电设计研究所 | System combination model credibility intelligent evaluation method based on deep learning |
| US10620531B1 (en) * | 2018-10-19 | 2020-04-14 | Cadence Design Systems, Inc. | Error-controlled discretization of parametric curves for integrated circuit design and manufacture |
| CN111368456A (en) * | 2020-03-19 | 2020-07-03 | 上海机电工程研究所 | Evidence theory-based reliability assessment method and system for blue square model |
| EP3757698A1 (en) * | 2019-06-28 | 2020-12-30 | Robert Bosch GmbH | Method and device for evaluating and selecting signal comparison metrics |
| CN112311606A (en) * | 2020-11-12 | 2021-02-02 | 中国科学院计算技术研究所 | Method for constructing virtual-real decoupling simulation network |
| CN112528159A (en) * | 2020-12-24 | 2021-03-19 | 北京百度网讯科技有限公司 | Characteristic quality evaluation method and device, electronic equipment and storage medium |
| KR20210048948A (en) * | 2019-10-24 | 2021-05-04 | 서울대학교산학협력단 | Estimation method to analyze parameter and prediction uncertainty of River Storage Mode using the Bayesian inference and segment mixture likelihood |
| CN113283133A (en) * | 2021-05-17 | 2021-08-20 | 杜玉玲 | Sensor grid discretization error evaluation method based on ANSYS software |
| CN114443091A (en) * | 2021-12-30 | 2022-05-06 | 咪咕音乐有限公司 | Webpage quality evaluation method and device, terminal equipment and readable storage medium |
| WO2022165811A1 (en) * | 2021-02-07 | 2022-08-11 | 华为技术有限公司 | Simulation method and related device |
-
2023
- 2023-01-12 CN CN202310066372.3A patent/CN116187034B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10366362B1 (en) * | 2012-10-18 | 2019-07-30 | Featuremetrics, LLC | Feature based modeling for forecasting and optimization |
| CN107231408A (en) * | 2017-05-03 | 2017-10-03 | 同济大学 | The building method for the connectivity modeling predicted in urban road based on vehicle-state |
| CN109190901A (en) * | 2018-08-03 | 2019-01-11 | 中国船舶工业综合技术经济研究院 | The credible evaluation method of reliability assessment result based on multi objective measurement |
| US10620531B1 (en) * | 2018-10-19 | 2020-04-14 | Cadence Design Systems, Inc. | Error-controlled discretization of parametric curves for integrated circuit design and manufacture |
| EP3757698A1 (en) * | 2019-06-28 | 2020-12-30 | Robert Bosch GmbH | Method and device for evaluating and selecting signal comparison metrics |
| KR20210048948A (en) * | 2019-10-24 | 2021-05-04 | 서울대학교산학협력단 | Estimation method to analyze parameter and prediction uncertainty of River Storage Mode using the Bayesian inference and segment mixture likelihood |
| CN110889207A (en) * | 2019-11-08 | 2020-03-17 | 江南机电设计研究所 | System combination model credibility intelligent evaluation method based on deep learning |
| CN111368456A (en) * | 2020-03-19 | 2020-07-03 | 上海机电工程研究所 | Evidence theory-based reliability assessment method and system for blue square model |
| CN112311606A (en) * | 2020-11-12 | 2021-02-02 | 中国科学院计算技术研究所 | Method for constructing virtual-real decoupling simulation network |
| CN112528159A (en) * | 2020-12-24 | 2021-03-19 | 北京百度网讯科技有限公司 | Characteristic quality evaluation method and device, electronic equipment and storage medium |
| WO2022165811A1 (en) * | 2021-02-07 | 2022-08-11 | 华为技术有限公司 | Simulation method and related device |
| CN113283133A (en) * | 2021-05-17 | 2021-08-20 | 杜玉玲 | Sensor grid discretization error evaluation method based on ANSYS software |
| CN114443091A (en) * | 2021-12-30 | 2022-05-06 | 咪咕音乐有限公司 | Webpage quality evaluation method and device, terminal equipment and readable storage medium |
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