WO2021049858A1 - Procédé de construction d'un modèle d'ordre réduit combinant des données de mesure de champ et une analyse cae par l'intermédiaire d'un pod couplé - Google Patents

Procédé de construction d'un modèle d'ordre réduit combinant des données de mesure de champ et une analyse cae par l'intermédiaire d'un pod couplé Download PDF

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WO2021049858A1
WO2021049858A1 PCT/KR2020/012136 KR2020012136W WO2021049858A1 WO 2021049858 A1 WO2021049858 A1 WO 2021049858A1 KR 2020012136 W KR2020012136 W KR 2020012136W WO 2021049858 A1 WO2021049858 A1 WO 2021049858A1
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principal component
measurement data
field measurement
pod
data
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PCT/KR2020/012136
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Korean (ko)
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허강열
한우주
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포항공과대학교 산학협력단
주식회사 페이스
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Publication of WO2021049858A1 publication Critical patent/WO2021049858A1/fr

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0221—Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods

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  • the present invention relates to a method for constructing an order reduction model, and more particularly, to a method for constructing an order reduction model that combines field measurement data and CAE analysis.
  • the order reduction model is a technique for obtaining numerical analysis results in a short time by simplifying the solution of the physical and mathematical models used in CAE without directly obtaining the solution. If the order reduction model is used, real-time monitoring and response in the field becomes possible.
  • Patent Document 1 US 2016-0275234 A1 (2016.09.22)
  • Patent Document 2 US 2018-0210436 A1 (2018.07.26)
  • Patent Document 3 US 2019-0122416 A1 (2019.04.25)
  • Non-Patent Document 1 T. Braconniera, M. Ferriera, J-C. Jouhauda, M. Montagnaca, P. Sagautb, "Towards an adaptive POD/SVD surrogate model for aeronautic design", Computers and Fluids, 2010
  • Non-Patent Document 2 Asal Kaveh, Wagdi G. Habashi, Zhao Zhan, "Combining CFD, EFD and FFD Data via Gappy Proper Orthogonal Decomposition", AIAA, 2018
  • the present invention was conceived to solve the above problems, and an object of the present invention is to construct an order reduction model that combines on-site measurement data and CAE analysis.
  • the goal is to provide a method of building a reduction-order model so that anyone can easily grasp information on all variables of interest in real time.
  • the method of constructing a reduction-order model selects an operating variable or condition that determines the operating condition of a target product or facility as a parameter, and analyzes CAE for cases sampled in a given parameter space.
  • CAE analysis step of performing A principal component analysis step of extracting a principal component of the CAE analysis result performed in the CAE analysis step; And combining field measurement data to obtain a principal component constant value by introducing field measurement data to the principal component extracted in the principal component analysis step; wherein the principal component analysis step and the field measurement data combination step include: It is characterized in that the principal component extraction and principal component constant values are obtained by simultaneously relating all variables.
  • the number of variables of the field measurement data is less than or equal to the number of variables of interest.
  • a procedure of normalizing the absolute size of data values between different variables may be performed in advance.
  • the order reduction model According to the method of constructing the order reduction model according to the present invention, it is possible to construct an order reduction model in which CAE results for all types of related variables can be obtained quickly and easily without deteriorating accuracy and reliability with only field measurement data for limited types of variables. You will be able to.
  • FIG. 1 is a flowchart sequentially showing steps of performing a method for constructing an order reduction model according to the present invention.
  • FIG. 2 is a diagram showing an example of reconstructing a temperature distribution by a gappy-POD method using only cell data corresponding to 0.1% of an original temperature distribution in a pulverized coal burner.
  • FIG. 3 is a diagram showing a combination of principal components to which temperature and oxygen mass fraction data are connected for a related-POD.
  • FIG. 4 is a diagram showing a result of predicting a temperature and oxygen mass fraction distribution in a pulverized coal burner using the order reduction model constructed according to the present invention.
  • FIG. 1 is a flowchart sequentially showing steps of performing a method for constructing an order reduction model according to the present invention.
  • a method for constructing a reduced-order model includes a CAE analysis step (S100), a principal component analysis step (S200), and a field measurement data combining step (S300).
  • Each step of the present invention can be performed by a program in which the present invention is implemented on a computer, and field measurement data can be obtained in real time by various sensors provided in the field.
  • CAE analysis step S100 an operation variable or condition that determines the operation condition of a target product or facility is selected as a parameter, and CAE analysis is performed on cases sampled in a given parameter space.
  • the parameter is an operation variable or condition that determines the internal state of the target facility.
  • the parameter in the case of a burner, it may be a fuel injection amount, an air flow rate, an air temperature, a turning ratio, and the like.
  • sampling is a procedure for pre-determining a combination of parameters for an appropriate number of cases required to build a degree reduction model, such as Random Sampling or Latin Hyper Cube Sampling (hereinafter'LHS').
  • LHS is a method of dividing each parameter into a range with the same probability and then sampling variables within each range according to a specific correlation, and is known to reproduce the mean value better than complete randomization.
  • Principal component analysis step (S200) is a step of performing a principal component analysis (Principal Component Analysis) to extract a principal component of the CAE analysis result performed in the CAE analysis step (S100).
  • principal component analysis is an analysis method that finds a common denominator of data obtained by performing CAE analysis on a parameter extracted by sampling in the CAE analysis step (S100), and is a concept similar to obtaining a maximum common divisor. That is, just as the original number can be obtained by multiplying the value by an appropriate constant when the greatest common divisor is found, the original data can be reproduced by multiplying and adding all the constants corresponding to each principal component.
  • the principal component analysis is preferably performed based on a Proper Orthogonal Decomposition (hereinafter referred to as'POD') method.
  • 'POD' Proper Orthogonal Decomposition
  • POD is the CAE analysis data ( ) As the main component ( ) In combination.
  • Is Orthogonal matrix obtained by decomposing Eigenvalue Is Orthogonal matrix obtained by decomposing the eigenvalues
  • Is Wow Each represents a diagonal matrix using the square root of the eigenvalues resulting from decomposing the eigenvalues as the diagonal element, where the POD main component Can be selected as follows.
  • the field measurement data is introduced into the principal component extracted in the principal component analysis step (S200), and the principal component constant value ( ).
  • the principal component constant value can be obtained by performing a gappy-POD.
  • Gappy-POD is a concept first introduced in the field of facial recognition (Emerson and Sirovich, "Karhunen-Loeve procedure for gappy data” 1996), and is a technique for inferring original data using only some lost data or measured values. For example, if temperature measurement data is given near a wall through an actual experiment, the principal component constant value can be calculated so that the measured data at the temperature measurement location and the predicted value coincide. In other words, the temperature measurement data at a specific location And the main component constructed using the temperature distribution result by CAE analysis If you say,
  • the distribution reflecting the original data or measured data can be inferred.
  • FIG. 2 shows an example of reconstructing the temperature distribution by the gappy-POD method using only cell data corresponding to 0.1% of the original temperature distribution in the pulverized coal burner.
  • the present invention introduces a method of extending the principal component analysis called a coupled-POD.
  • association-POD results of variables of interest are combined into one data during principal component analysis, and then principal component analysis is performed on the combined data. For example, if temperature data, carbon monoxide data, and oxygen concentration data are combined into one to perform a principal component analysis, and then go through the gappy-POD procedure using only the temperature measurement data, not only the temperature distribution, but also the distribution of carbon monoxide and oxygen concentration is obtained. You will be able to.
  • the measurement data includes not only temperature but also carbon monoxide data, a more accurate result value can be inferred by performing the gappy-POD procedure using both data for these two variables.
  • This normalization procedure can be performed by dividing different variables by the reference value of the variable. For example, when performing a correlation-POD by combining the temperature and oxygen data values as they are, the value of the temperature data is larger than the value of the oxygen data. Therefore, since information on the oxygen data may be lost, a procedure such as dividing the temperature data and the oxygen data by the maximum value of each value to have a similar size and adding them can be performed in advance.
  • Subject is a laboratory-scale pulverized coal burner with two parameters: oxygen (or air) flow rate and turn ratio, and 10 sample cases were determined via LHS for a given parameter operating range.
  • OpenFOAM was used as a CAE analysis tool, and a specialized simpleCoalCombustionFoam solver was used for the steady-state pulverized coal combustion analysis provided by OpenFOAM.
  • the main component was extracted by relating the distribution of the mass fraction of oxygen to the temperature so that both the temperature and the distribution of the mass fraction of oxygen can be obtained using only real-time temperature measurement data.
  • FIG. 3 shows a combination of principal components to which temperature and oxygen mass fraction data are linked for association-POD.
  • the order reduction model was constructed to obtain the temperature and oxygen mass fraction distribution reflecting the real-time field temperature data.
  • 4 shows the results of predicting the temperature and oxygen mass fraction distribution in the pulverized coal burner using the order reduction model constructed according to this application example.
  • the main component When constructing a vector that connects the oxygen mass fraction as well as the temperature By using, it is possible to obtain a distribution reflecting the real-time field measurement data at the same time as the oxygen mass fraction using only the temperature measurement data.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Regulation And Control Of Combustion (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

La présente invention concerne, dans la construction d'un modèle d'ordre réduit combinant des données de mesure de champ et une analyse CAE, un procédé de construction d'un modèle d'ordre réduit, le modèle d'ordre réduit, par l'intermédiaire d'un POD couplé, étant hautement précis et permettant à quiconque d'identifier facilement des informations sur toutes les variables d'intérêt en temps réel, même au moyen uniquement de données de mesure de champ pour des types limités de variables.
PCT/KR2020/012136 2019-09-09 2020-09-09 Procédé de construction d'un modèle d'ordre réduit combinant des données de mesure de champ et une analyse cae par l'intermédiaire d'un pod couplé WO2021049858A1 (fr)

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KR1020190111203A KR102048243B1 (ko) 2019-09-09 2019-09-09 연관-pod를 통하여 현장 측정 데이터와 cae 해석을 결합한 차수 감축 모델 구축 방법
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KR102048243B1 (ko) * 2019-09-09 2019-11-25 주식회사 페이스 연관-pod를 통하여 현장 측정 데이터와 cae 해석을 결합한 차수 감축 모델 구축 방법
KR102266279B1 (ko) * 2019-12-18 2021-06-17 포항공과대학교 산학협력단 비정상상태를 구현하기 위한 차수 감축 모델 구축 방법
KR102261942B1 (ko) 2020-12-24 2021-06-07 주식회사 페이스 다중물리 설비 시스템에 대한 차수 감축 모델, 측정 데이터 및 기계학습 기법을 융합한 디지털 트윈 구축 방법
CN113642105B (zh) * 2021-08-03 2023-10-27 中国船舶重工集团公司第七一九研究所 船舶动力系统的多尺度模型构建方法、装置及电子设备
KR20230095383A (ko) * 2021-12-22 2023-06-29 포항공과대학교 산학협력단 선형 투영 및 비선형 투영 방식을 혼합한 차수 감축 모델 구축 방법
KR102484587B1 (ko) * 2022-07-29 2023-01-04 주식회사 페이스 시뮬레이션 기반의 주성분 벡터와 측정 데이터 기반의 기계학습에 의한 주성분 상수를 활용한 차수 감축 모델 구축 방법
WO2024025382A1 (fr) * 2022-07-29 2024-02-01 주식회사 페이스 Système jumeau numérique et procédé permettant une surveillance en temps réel, une prédiction de fonctionnement virtuel et un service de fonctionnement d'optimisation d'équipement sur site
KR102601707B1 (ko) 2022-07-29 2023-11-13 주식회사 페이스 현장 설비의 실시간 모니터링, 가상 운전 예측 및 최적화 운전 서비스가 가능한 디지털 트윈 시스템 및 방법

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CN117332200B (zh) * 2023-10-09 2024-05-14 北京航空航天大学 一种基于最大pod系数的物理场重构和预测方法

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