WO2022191199A1 - 物理定数の推定値取得方法 - Google Patents

物理定数の推定値取得方法 Download PDF

Info

Publication number
WO2022191199A1
WO2022191199A1 PCT/JP2022/010043 JP2022010043W WO2022191199A1 WO 2022191199 A1 WO2022191199 A1 WO 2022191199A1 JP 2022010043 W JP2022010043 W JP 2022010043W WO 2022191199 A1 WO2022191199 A1 WO 2022191199A1
Authority
WO
WIPO (PCT)
Prior art keywords
value
temperature
estimated value
physical constant
solid substance
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/JP2022/010043
Other languages
English (en)
French (fr)
Japanese (ja)
Inventor
正和 柴原
一樹 生島
陸 高橋
悠二 木谷
悠暉 山内
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
University Public Corporation Osaka
Original Assignee
University Public Corporation Osaka
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by University Public Corporation Osaka filed Critical University Public Corporation Osaka
Priority to EP22767153.4A priority Critical patent/EP4306946A4/en
Priority to US18/281,060 priority patent/US20240160809A1/en
Priority to JP2023505582A priority patent/JP7488523B2/ja
Publication of WO2022191199A1 publication Critical patent/WO2022191199A1/ja
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/08—Thermal analysis or thermal optimisation

Definitions

  • the present invention provides a method for obtaining an estimated value of a physical constant at a predetermined temperature, a program for causing a computer to execute the method, a recording medium for storing the program, a device comprising an arithmetic unit for executing the program, and the method.
  • the present invention relates to a method for manufacturing a processed product that has been damaged and a method for monitoring fatigue damage.
  • Non-Patent Document 1 a method of obtaining predicted values by simulation using finite element method (FEM) analysis is widely used.
  • FEM finite element method
  • the strength of the hull is a very important parameter from the viewpoint of ensuring safety, but when designing the hull structure, it is necessary to consider various external forces such as the weight of cargo, fluctuating loads due to waves, and loads due to hull motion. Therefore, it was very difficult to accurately evaluate the strength of the hull.
  • FEM analysis is used for hull strength evaluation, and the hull structure is designed based on the FEM analysis results.
  • the stress applied to the hull by FEM analysis is estimated to be larger than the actual phenomenon, the strength of the hull will be excessively increased by increasing the plate thickness, etc. As a result, fuel consumption will be worsened due to the increase in weight. was the problem.
  • the protected hot plate method is known as a method for measuring the thermal conductivity of solid materials.
  • a problem with this method is the use of complex test equipment. Furthermore, a test piece of 300 mm or more is required, and there is a problem that the size of the test piece is limited. Furthermore, in the above method, the maximum operating temperature is 700° C., making measurement in a higher temperature range difficult.
  • Another known method for measuring the thermal conductivity of solid materials is the laser flash method.
  • a specimen having a thickness of 1 mm or more is required, and there is a problem in that the size of the specimen is limited.
  • errors there is a tendency for errors to increase due to heat loss due to radiation, etc., resulting in the problem of low accuracy at high temperatures.
  • the object of the present invention is to obtain estimated values of physical constants of solid substances that are temperature-dependent, in accordance with actual phenomena, by accurately reproducing changes accompanying temperature rise up to high-temperature regions in cyberspace.
  • Another object of the present invention is to accurately estimate the physical constants of solid substances that have temperature dependence, using a simple device, to accurately estimate changes due to temperature rise up to a high temperature range, and to obtain estimated values that are in line with actual phenomena.
  • Another object of the present invention is to provide a computer program for obtaining an estimated value of a temperature-dependent physical constant of a solid substance at a predetermined temperature, which corresponds to an actual phenomenon.
  • Another object of the present invention is to provide a recording medium storing the computer program.
  • the inventors of the present invention have found that the temperature-dependent physical constants of solid substances are the predicted value F obtained by FEM analysis, and the actual measured value Y obtained by simple measurement. It was found that the estimated value A obtained by assimilating the data to can reproduce the phenomenon more realistically in cyberspace. The present invention has been completed based on these findings.
  • the present invention is a method for obtaining an estimated value A at a predetermined temperature of a physical constant having temperature dependence of a solid substance, comprising: Provided is an estimated value obtaining method for obtaining an estimated value A by assimilating the predicted value F of the physical constant obtained by the finite element method analysis with the measured value Y of the physical constant.
  • the present invention also obtains the estimated value A from room temperature to the temperature at which the solid substance dissolves at every ⁇ t° C., and the estimated value A at (t ⁇ t)° C.
  • t ⁇ t is the physical constant t given as an initial value to the finite element method analysis at °C to obtain the predicted value Ft, and the obtained predicted value Ft is data assimilated to the measured value Yt at t °C to obtain the estimated value At at t °C.
  • the present invention also provides a method for obtaining the estimated value that performs data assimilation using an ensemble Kalman filter.
  • the present invention also provides a method for obtaining the estimated value, wherein the finite element method analysis is heat conduction analysis or thermal elastic-plastic analysis by the finite element method.
  • the present invention also provides a program for causing a computer to acquire an estimated value at a predetermined temperature of a physical constant having temperature dependence of a solid substance, comprising: a first step of inputting the measured value Y of the physical constant into a computer; a second step of causing a computer to perform a finite element method analysis to obtain a predicted value F of the physical constant; a third step of causing a computer to perform data assimilation of the predicted value F to the measured value Y to obtain the estimated value A;
  • the present invention also provides a computer-readable recording medium for storing the program.
  • the present invention also provides a device comprising a computing unit that executes the program.
  • the present invention also provides a method for producing a processed product, which obtains a processed product of solid material through the following steps.
  • Step 1 Acquire an estimated value at a predetermined temperature of the temperature-dependent physical constant of the solid substance by the method for acquiring an estimated value
  • Step 2 Use the acquired estimated value to determine a processing method for the solid substance decide
  • the present invention also obtains an estimated value at a predetermined temperature of a temperature-dependent physical constant of a solid substance by the method for obtaining an estimated value, and uses the obtained estimated value to form a solid substance.
  • a fatigue damage monitoring method for monitoring the fatigue damage of a constructed structure.
  • the method of obtaining an estimated value of the present invention obtains an estimated value of a physical constant having a temperature dependence of a solid substance by assimilating the predicted value obtained by FEM analysis with the measured value using a simple device. The method. Then, according to the above method, an estimated value that is very close to the actual phenomenon is obtained.
  • the estimation value acquisition method of the present invention it is possible to estimate all physical constants that govern specific physical properties that change with temperature rise. Therefore, the estimated value acquisition method of the present invention can be utilized in any situation by selecting physical constants.
  • the thermal conductivity of a solid substance can be estimated with high accuracy, and based on the obtained estimated value, a processing method for the solid substance can be set. If so, it is possible to improve production efficiency and reduce costs.
  • FIG. 4 is an explanatory diagram of a data assimilation method using an ensemble Kalman filter
  • Fig. 3 is a flow chart of one embodiment of a method for obtaining an estimate according to the present invention
  • FIG. 4 is a diagram showing an example of a heat input point of a solid substance in the present invention
  • It is a figure which shows an example of the model diagram used for the acquisition method of the estimated value of this invention.
  • FIG. 10 is a diagram showing that by repeating FEM analysis and data assimilation a plurality of times, the obtained estimated value approximates the true value more.
  • FIG. 4 is a diagram showing that the estimated values of specific heat (c) and thermal conductivity ( ⁇ ) obtained by the estimated value acquisition method according to the present invention were very close to the true values over the entire temperature range.
  • FIG. 10 is a diagram showing that the estimation results of temperature histories at points A, B, and C obtained in Example 2 were very close to the true values.
  • FIG. 10 is a diagram showing that the estimation result of the thermal conductivity at point A obtained in Example 2 was very close to the true value.
  • FIG. 10 is a diagram showing that the estimation result of the specific heat obtained in Example 3 was very close to the true value.
  • a method for obtaining an estimated value of the present invention acquires an estimated value A of a physical constant having temperature dependence of a solid substance at a predetermined temperature (or an estimated value A of a physical constant that varies with heat input at a predetermined temperature).
  • a method In this method, the predicted value F of the physical constant obtained by FEM analysis is assimilated into the actual measured value Y of the physical constant to acquire the estimated value A of the physical constant.
  • the predetermined temperature is, for example, a temperature within a range from room temperature (20°C) to a temperature at which the solid substance dissolves (for example, 1300°C when the solid substance is SUS304).
  • the solid substance is not particularly limited, and examples thereof include stainless steel (eg, SUS304, SUS316, etc.), steel, steel, iron, aluminum, nickel, copper, zinc, titanium, magnesium, tin, molybdenum, and alloys thereof.
  • stainless steel eg, SUS304, SUS316, etc.
  • steel steel, iron, aluminum, nickel, copper, zinc, titanium, magnesium, tin, molybdenum, and alloys thereof.
  • a metal material is mentioned.
  • the above physical constants include all physical constants that govern changes in the physical properties of solid substances as the temperature rises.
  • Examples of the physical constants include specific heat, thermal conductivity, Young's modulus, Poisson's ratio, coefficient of linear expansion, yield stress, and the like.
  • the predicted value F is data assimilated to the measured value Y for the physical constant at at least one temperature in the temperature range from room temperature to the temperature at which the solid substance melts.
  • the method of data assimilation of the predicted value F to the measured value Y is selected from the range of 10 to 500°C, such as every 10°C, every 50°C, every 100°C, every 200°C, and every 500°C. It is preferable to assimilate the predicted value F of the physical constant into the measured value Y for each specific temperature ( ⁇ t ° C.), and from the viewpoint of reducing the calculation load and estimating with high accuracy, the physical constant Data assimilation of the predicted value F of to the measured value Y is particularly preferred. Therefore, the ⁇ t°C is preferably 100°C.
  • the data assimilation is performed every ⁇ t° C. in the temperature range from room temperature to the temperature at which the solid substance dissolves, from the viewpoint of obtaining highly accurate estimated values while reducing the computational load. It is preferable to obtain an estimate A.
  • the FEM analysis may be performed with the initial value fixed, thereby obtaining the predicted value F from room temperature to the temperature at which the solid substance dissolves. Meanwhile, from the viewpoint of reducing the error and obtaining a highly accurate predicted value F, the initial value is reset periodically (for example, every ⁇ t ° C.) in the temperature range from room temperature to where the solid substance melts. FEM analysis is preferably performed.
  • the estimated value A t- ⁇ t at (t ⁇ t)° C. is preferably given as an initial value to the FEM analysis of the physical constants at t° C.
  • the estimated value A is obtained at every ⁇ t° C. from room temperature to the temperature at which the solid substance dissolves
  • the estimated value A t ⁇ t at (t ⁇ t)° C. is obtained as follows: Given as an initial value to the FEM analysis of the physical constant at t ° C. to obtain a predicted value F t , the obtained predicted value F t is data assimilated to the measured value Y t of the physical constant at t ° C., It is preferable to obtain an estimate of A t . Both (t ⁇ t)° C. and t° C. fall within the range from room temperature to the temperature at which the solid substance dissolves.
  • ⁇ t°C is, for example, a temperature selected from 10 to 500°C, preferably 100°C.
  • the method for acquiring the estimated value is to acquire the estimated value A from the low temperature side every ⁇ t ° C. from room temperature to the temperature at which the solid substance dissolves, and to obtain the physical constant at a predetermined temperature [t (° C.)].
  • the estimated value A t- ⁇ t at the temperature [(t- ⁇ t) ° C.] closest to the temperature is given as an initial value to obtain the predicted value F t . It is preferable to assimilate the obtained predicted value F t with the measured value Y t at the temperature [t (° C.)] to obtain the estimated value A t at the temperature [t (° C.)].
  • Both the (t ⁇ t)° C. and t° C. are included in the range from room temperature to the temperature at which the solid substance dissolves.
  • ⁇ t°C is, for example, a temperature selected from 10 to 500°C, preferably 100°C.
  • the initial values given in the FEM analysis of the physical constants at a predetermined temperature (t2) above 100° C. and up to the melting temperature of the solid substance are: Starting the estimation from the room temperature (or from the low temperature side) and using the estimated value A at the temperature closest to the predetermined temperature (t2) among the already obtained estimated values A as the initial value corrects the error. This is preferable from the viewpoint of obtaining a more accurate estimated value.
  • Step 1 A step of obtaining an estimated value A t1 at a predetermined temperature (t1) from room temperature to 100° C.
  • Step 2 Predetermined temperature exceeding 100 ° C to the temperature at which the solid substance melts ( This is a step of obtaining an estimated value A t2 at t2).
  • the estimated value at the temperature closest to the predetermined temperature (t2) is given as an initial value, and the physical constant at the temperature (t2) is calculated.
  • a predicted value F t2 is obtained by FEM analysis, and the obtained predicted value F t2 is data assimilated to the measured value Y t2 to obtain an estimated value A t2
  • the predicted value F t1 is the predicted value F at the temperature (t1), and the measured value Y t1 is the measured value Y at the temperature (t1).
  • the predicted value F t2 is the predicted value F at the temperature (t2), and the measured value Y t2 is the measured value Y at the temperature (t2).
  • the method for obtaining the estimated value it is particularly preferable to repeatedly obtain the estimated value A in step 2 by setting a predetermined temperature every ⁇ t° C. from the viewpoint of accurately estimating the physical constant. .
  • the predicted value F at a given temperature is obtained by FEM analysis.
  • the estimated value A is obtained in order from the low temperature side to the high temperature side, and the estimated value A obtained by estimating the physical constant at the previous temperature is used as the initial value for the FEM analysis of the physical constant at the next temperature. to obtain the predicted value F.
  • the estimated value A is obtained from room temperature to the temperature at which the solid substance dissolves, in order from the low temperature side, every ⁇ t ° C.
  • FEM analysis of the physical constant at t ° C. is performed, the already obtained estimated value Of A, it is preferable to give the estimated value A at the temperature [(t ⁇ t)° C.] closest to t° C. as the initial value.
  • the initial value is the temperature one before (that is, (t2 ⁇ t)°C)
  • the estimated value of A t2 ⁇ t obtained by performing FEM analysis and data assimilation of the physical constants are given.
  • the predicted value F t2 at t2°C is obtained.
  • the estimated value A t2 obtained by data assimilation of the predicted value F t2 at t2 ° C. to the actual value Y t2 is the initial value for the FEM analysis of the physical constant at the next temperature (that is, (t2 + ⁇ t) ° C.) give as
  • the number of elements and the number of nodes can be set appropriately. As the number of elements and nodes increases, the prediction accuracy increases, but the computational load tends to increase.
  • an analysis program can be appropriately selected and used according to the physical constant for which an estimated value is to be obtained. For example, when estimating specific heat or thermal conductivity, it is preferable to select an analysis program capable of executing thermal conduction analysis or thermal elastic-plastic analysis.
  • ensemble members (the number of members is, for example, 2 to 10, preferably 3 to 8) are given as initial values, and corresponding to each member Preferably, FEM analysis is performed.
  • the idealized explicit method FEM is a method using the dynamic explicit method FEM, and is a method in which analysis is performed by independent calculation for each element and each analysis degree of freedom.
  • the FEM analysis is performed by setting state vectors for the nodes of each element obtained by mesh division.
  • the state vector has a degree of freedom and a displacement vector.
  • the degrees of freedom are appropriately selected and set according to the properties of physical constants to be predicted. For example, when predicting physical constants that govern heat conduction, nodal temperatures are set. Also, when predicting physical constants that govern dynamic phenomena, nodal temperatures and strains are set. Then, the desired physical constant can be added to the displacement vector.
  • the state vector of each node when estimating the specific heat (c t ) and the thermal conductivity ( ⁇ t ) at the same time as the physical constants governing heat conduction can be expressed, for example, by the following equation (1).
  • the state vector represented by the following formula (1) includes the node temperature ( ⁇ dof ) as the degree of freedom, and the specific heat (c t ) and thermal conductivity ( ⁇ t ) as the displacement vector.
  • the state vector of each node when estimating the Young's modulus (E) and the Poisson's ratio (v) simultaneously as physical constants governing the dynamic phenomenon can be expressed by the following equation (2), for example.
  • the state vector represented by the following formula (2) includes nodal temperature ( ⁇ dof ) and strain ( ⁇ ) as degrees of freedom, and Young's modulus (E) and Poisson's ratio (v) as a displacement vector.
  • Data assimilation is a technique for correcting a physical model by reflecting measured values in simulations based on optimization theory. Data assimilation of the predicted value F t to the measured value Y t makes it possible to modify the predicted value F t to bring it closer to the actual phenomenon. Therefore, the estimated value A t obtained by data assimilation is highly accurate compared to the measured value Y t .
  • Data assimilation is performed using, for example, a Kalman filter or an ensemble Kalman filter.
  • a Kalman filter or an ensemble Kalman filter.
  • an ensemble Kalman filter it is preferable to use an ensemble Kalman filter.
  • the estimated value A t is obtained by weighting the predicted value F t and the measured value Y t with the Kalman gain.
  • the Kalman gain is a weighting coefficient for minimizing the error between the predicted value Ft and the measured value Yt .
  • the estimated value A t at the predetermined temperature (t) is obtained from the following equation (3).
  • Figure 1 shows a diagram explaining the data assimilation method using the ensemble Kalman filter.
  • Data assimilation may be performed at least once for each predetermined temperature, but it is preferable to perform data assimilation multiple times in order to further improve the accuracy of the obtained estimated value.
  • the number of iterations of data assimilation until the estimation result converges, that is, the estimated value obtained by the first data assimilation is given as the initial value and the FEM analysis is performed again. It is preferable to perform the assimilation work, for example, about 2 to 50 times (preferably 2 to 5 times) until the displacement satisfies the static equilibrium state. After a static equilibrium state is obtained, one can proceed to the next predetermined temperature.
  • FIG. 2 shows a flowchart of one embodiment of the method for obtaining the estimated value.
  • the temperature at which the solid substance dissolves is, for example, 1300° C. when the solid substance is SUS304.
  • the actual measured value Y t at the predetermined temperature (t) includes the actual measured value Y t during the heating process, that is, the actual measured value Y t at t ° C. when the temperature rises, and the actual measured value Y t during the cooling process, that is, when the temperature drops at t° C. of Y t ".
  • a combination of these may be included.
  • These can be selected according to the type of physical constant for which an estimated value is to be obtained. For example, when estimating thermal efficiency, etc., it is better to use the measured values in the heating process, and when estimating the heat conduction coefficient, specific heat, etc., it is better to use the measured values in the cooling process. It is preferable in that a more accurate estimated value can be obtained.
  • the measured value Y t includes the temperature history from room temperature (20° C.) to t° C. of the temperature-dependent physical constants of solid substances, and the physical constants of solid substances that fluctuate along with the temperature history.
  • the actually measured value Y t can be obtained simply by inputting heat of t° C. into a solid substance and actually measuring its temperature history and physical constants.
  • a solid material for heat input for example, a thin plate (size is 200 mm ⁇ 200 mm ⁇ 10 mm, for example) made of the solid material for which the estimated value is to be obtained can be used.
  • the actual measurement points are at least 2 points, for example, 2 to 10 points, preferably 2 to 6 points, from the viewpoint of reducing the calculation load and estimating with high accuracy.
  • the point where the room temperature rises to t ° C. is the closest measurement point 1 to the heat input point, and the position gradually moves away from the heat input point. It is preferable to provide a plurality of measurement points such as measurement points 2, 3, 4, . . . As a result, from a single heat input experiment, it is possible to obtain multiple temperature histories from room temperature (20°C) with an upper limit of t°C, and the fluctuations in physical constants accompanying these temperature histories, improving the accuracy of estimated values. To contribute.
  • thermocouples there are no particular restrictions on the method of inputting heat into a solid substance, and examples include methods using thermocouples. Further, the heat may be input at a point using a fixed heat source, or may be input through a wire using a line heat source.
  • the estimated value acquisition method of the present invention can be used, for example, in a method for manufacturing processed products. More specifically, by using the estimated values obtained by the estimated value acquisition method, it is possible to design an optimum processing method according to the physical phenomenon of the solid substance. This makes it possible to improve production efficiency and reduce costs.
  • the estimated value obtained by the method for obtaining the estimated value it is possible to accurately predict the portion of the structure formed of the solid material that is prone to fatigue damage. By focused monitoring, the occurrence of fatigue damage can be monitored efficiently and cost-effectively.
  • a program of the present invention is a program for causing a computer to acquire an estimated value at a predetermined temperature of a physical constant having temperature dependence of a solid substance, a first step of inputting the measured value Y of the physical constant into a computer; a second step of causing a computer to perform an FEM analysis to obtain a predicted value F of the physical constant; a third step of causing a computer to perform data assimilation of the predicted value F to the measured value Y to obtain the estimated value A; including.
  • the first step is to input the measured value Y into the computer.
  • Data of the measured value Y to be input is obtained by the measuring method of the measured value Y described above.
  • the second step is to have the computer perform the FEM analysis to obtain the predicted value F.
  • the third step is to obtain an estimated value A by causing the computer to perform data assimilation of the predicted value F to the measured value Y.
  • the second step and the third step are preferably repeatedly executed a plurality of times until the estimation result converges.
  • the program is preferably a program for causing a computer to acquire an estimated value at a predetermined temperature of a physical constant having temperature dependence of a solid substance, Step I of inputting the measured value Y of the physical constant at every ⁇ t° C. from room temperature to the temperature at which the solid substance melts into a computer; Step II of causing a computer to select an appropriate initial value and to perform an FEM analysis using the selected initial value to obtain a predicted value F of the physical constant; A step III in which the computer assimilates the predicted value F to the measured value Y to obtain the estimated value A; a step IV of causing a computer to execute the steps II and III every ⁇ t° C. in the range from room temperature to the temperature at which the solid substance dissolves; including.
  • Step I is a step of inputting into the computer the actual measured values of the physical constants from room temperature to the temperature at which the solid substance melts at every ⁇ t°C.
  • Step II The second step is to have the computer select appropriate initial values and perform FEM analysis using the selected initial values to obtain the predicted value F.
  • the initial values in the FEM analysis of the physical constants at a predetermined temperature from room temperature to 100° C., for example, it is preferable to use, for example, the physical constants in the literature or the measured values as the initial values.
  • the above literature value cannot be obtained, it is also possible to use the literature values of other solid substances with similar physical properties.
  • the estimated value A at the nearest temperature is preferable to use as the initial value.
  • the third step is a step in which the computer assimilates the predicted value F to the measured value Y to obtain the estimated value A.
  • FIG. More specifically, it is a step of assimilating the predicted value F at the predetermined temperature obtained in step II with the measured value Y of the temperature to obtain the estimated value A of the temperature.
  • FEM analysis is performed using the obtained estimated value A as an initial value until the estimation result converges, and the obtained predicted value F is data assimilated into the measured value Y. It is preferable to repeat the execution multiple times.
  • the fourth step is a step of causing the computer to execute the second step and the third step every ⁇ t° C. in the range from room temperature to the melting temperature of the solid substance.
  • every ⁇ t°C means, for example, every temperature selected from 10 to 500°C, preferably every 100°C.
  • the computer is not particularly limited as long as it is a device that can perform necessary calculations, and for example, an electronic calculator or the like is preferably used.
  • the program of the present invention by providing the program to a computer and having the computer execute it, it is possible to highly accurately estimate changes in physical constants associated with temperature rises in solid substances.
  • the program of the present invention is provided to a computer, for example, by recording it on a recording medium or via various transmission media.
  • the transmission medium is a communication medium in a computer network system for propagating and supplying program information as carrier waves, and includes, for example, wired lines such as optical fibers and wireless lines.
  • a recording medium of the present invention is a computer-readable recording medium for storing the program.
  • the recording medium is not particularly limited as long as it can provide the program to a computer and cause the computer to execute it. Examples include CD-ROM, flexible disk, hard disk, magnetic tape, magneto-optical disk, A non-volatile memory card and the like are included.
  • a device is a device (computer system) including an arithmetic unit that executes the above program.
  • the device is composed of, for example, a calculation unit, a display unit, a storage unit, a keyboard, a pointing device, and the like.
  • the device may include other components as desired.
  • the computing unit is the central processing unit that controls the entire computer.
  • the display unit is a device that displays various input conditions, analysis results, and the like in the control executed by the calculation unit.
  • the storage unit is a device that stores analysis results derived by the calculation unit.
  • a keyboard is a device used by an operator to input various input conditions.
  • a pointing device is composed of a mouse, a trackball, or the like.
  • each configuration and combination thereof of the present invention is an example, and addition, omission, replacement, and modification of the configuration are possible as appropriate within the scope of the present invention.
  • Example 1 The precision of the estimated values obtained by the method of the present invention for the specific heat (c) and thermal conductivity ( ⁇ ) of the stainless steel plate was evaluated by twin experiments.
  • the true values of the temperature history, specific heat, and thermal conductivity at the following six points were obtained by the following method.
  • heat is input by a linear heat source to raise the temperature from room temperature (20 ° C.) to 1300 ° C., and the temperature history at each point.
  • specific heat, and thermal conductivity were measured and used as true values.
  • the measurement was performed at six points near the heat source.
  • six points six points with temperatures of 20.5° C., 22° C., 28° C., 41° C., 68° C., and 100° C. at a heat input time of 10 seconds (see FIG. 3) were adopted. .
  • a pseudo measured value of 20 and a pseudo measured value of 100 were given as initial values, respectively, an analysis program capable of executing thermal conduction analysis or thermal elastic-plastic analysis was selected, element division was performed, and FEM analysis was performed (see Fig. 4). .
  • the number of nodes of the analysis model was set to 16000, and the number of elements was set to 12800. This gave a predicted value of 20 for specific heat and thermal conductivity at 20 °C and a predicted value of 100 for 100°C.
  • the estimated values 20 and 100 were obtained by assimilating the obtained predicted values 20 and 100 with the simulated measured values 20 and 100 , respectively.
  • SUS316 stainless steel plate
  • An analysis program capable of executing thermal conduction analysis or thermal elastic-plastic analysis was selected, element division was performed, and FEM analysis of temperature history and thermal conductivity was performed.
  • Example 1 except that the number of nodes in the analysis model was 15954 and the number of elements was 13070, and that the literature value of 0.355 was given as the initial value in the FEM analysis of the physical constants at 20 ° C. and 100 ° C.
  • the predicted values of the temperature history and thermal conductivity at each temperature from 20°C to 1100°C were calculated.
  • An estimated value was obtained by data assimilation of the above-mentioned pseudo measured value to the predicted value thus calculated.
  • Fig. 7 shows the estimation results and true values of the temperature history at the three points. It can be seen from FIG. 7 that the estimated values at the three points are all very close to the true values.
  • Fig. 8 shows the estimation result and the true value of the thermal conductivity at point A. From FIG. 8, it can be seen that all the estimated values are very close to the true values.
  • Example 3 The true values of the temperature history and specific heat at the following four points were obtained by the following method.
  • the temperature history and changes in specific heat were measured at each point as the temperature was raised, and these were taken as true values.
  • the measurement was performed on the center line of the heat input point, point A 7 mm away from the heat input point, point B further 2 mm away from point A, point C further 2 mm away from point B, and further 2 mm away from point C. It was performed at four points of the D point.
  • An analysis program capable of executing thermal conductivity analysis or thermal elastic-plastic analysis was selected, element division was performed, and FEM analysis of temperature history and specific heat was performed. Except for the fact that the number of nodes in the analysis model was 16673 and the number of elements was 18568, and that a random number of 0.45 to 0.67 was given as an initial value in the FEM analysis of physical constants at 20 ° C. and 800 ° C.
  • Example 1 the temperature history and the predicted value of the specific heat at each temperature of 20° C. and 800° C. were calculated. An estimated value was obtained by data assimilation of the above-mentioned pseudo measured value to the predicted value thus calculated.
  • Fig. 9 shows the estimation result and the true value of the specific heat. It can be seen from FIG. 9 that the estimated values are very close to the true values.
  • the estimation method of the present invention provides estimates that are very close to actual phenomena for all physical constants that govern specific physical properties that change with increasing temperature. Therefore, according to the estimated value acquisition method of the present invention, it is possible to accurately estimate the thermal conductivity of a solid substance. improvement and cost reduction can be realized.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Business, Economics & Management (AREA)
  • General Engineering & Computer Science (AREA)
  • Geometry (AREA)
  • Evolutionary Computation (AREA)
  • Computer Hardware Design (AREA)
  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • Operations Research (AREA)
  • General Business, Economics & Management (AREA)
  • Marketing (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Game Theory and Decision Science (AREA)
  • Development Economics (AREA)
  • Investigating Or Analyzing Materials Using Thermal Means (AREA)
PCT/JP2022/010043 2021-03-09 2022-03-08 物理定数の推定値取得方法 Ceased WO2022191199A1 (ja)

Priority Applications (3)

Application Number Priority Date Filing Date Title
EP22767153.4A EP4306946A4 (en) 2021-03-09 2022-03-08 Physical constant estimation value acquisition method
US18/281,060 US20240160809A1 (en) 2021-03-09 2022-03-08 Method of acquiring estimated value of physical constant
JP2023505582A JP7488523B2 (ja) 2021-03-09 2022-03-08 物理定数の推定値取得方法

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2021-037203 2021-03-09
JP2021037203 2021-03-09

Publications (1)

Publication Number Publication Date
WO2022191199A1 true WO2022191199A1 (ja) 2022-09-15

Family

ID=83226775

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2022/010043 Ceased WO2022191199A1 (ja) 2021-03-09 2022-03-08 物理定数の推定値取得方法

Country Status (4)

Country Link
US (1) US20240160809A1 (https=)
EP (1) EP4306946A4 (https=)
JP (1) JP7488523B2 (https=)
WO (1) WO2022191199A1 (https=)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2023142321A (ja) * 2022-03-24 2023-10-05 株式会社構造計画研究所 シミュレーション装置、シミュレーション方法及びプログラム

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2003194637A (ja) * 2001-12-26 2003-07-09 Toshiba Corp 有限要素法による残留応力解析方法
JP2005083810A (ja) * 2003-09-05 2005-03-31 Toshiba Corp 伝熱解析方法、伝熱解析プログラムを記録した記録媒体、伝熱解析装置および残留応力解析方法
JP2006064413A (ja) * 2004-08-24 2006-03-09 Shimane Univ 比熱および熱伝導率の測定方法。
JP2006337233A (ja) * 2005-06-03 2006-12-14 Toshiba Corp 残留応力評価方法、残留応力評価プログラムを記録した記録媒体および残留応力評価装置
WO2016031174A1 (ja) * 2014-08-27 2016-03-03 日本電気株式会社 シミュレーション装置、シミュレーション方法、および、記憶媒体
JP2020201146A (ja) * 2019-06-11 2020-12-17 株式会社構造計画研究所 パラメータ推定装置、パラメータ推定方法及びプログラム
JP2021037203A (ja) 2019-09-05 2021-03-11 Ｎｅｃエンベデッドプロダクツ株式会社 モータシステム、回胴式遊技機、制御方法及びプログラム

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4130156B2 (ja) * 2003-05-30 2008-08-06 富士通株式会社 物性値測定装置および物性値測定方法
JP2005164355A (ja) * 2003-12-02 2005-06-23 Olympus Corp 材料接合部評価方法および成形用金型

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2003194637A (ja) * 2001-12-26 2003-07-09 Toshiba Corp 有限要素法による残留応力解析方法
JP2005083810A (ja) * 2003-09-05 2005-03-31 Toshiba Corp 伝熱解析方法、伝熱解析プログラムを記録した記録媒体、伝熱解析装置および残留応力解析方法
JP2006064413A (ja) * 2004-08-24 2006-03-09 Shimane Univ 比熱および熱伝導率の測定方法。
JP2006337233A (ja) * 2005-06-03 2006-12-14 Toshiba Corp 残留応力評価方法、残留応力評価プログラムを記録した記録媒体および残留応力評価装置
WO2016031174A1 (ja) * 2014-08-27 2016-03-03 日本電気株式会社 シミュレーション装置、シミュレーション方法、および、記憶媒体
JP2020201146A (ja) * 2019-06-11 2020-12-17 株式会社構造計画研究所 パラメータ推定装置、パラメータ推定方法及びプログラム
JP2021037203A (ja) 2019-09-05 2021-03-11 Ｎｅｃエンベデッドプロダクツ株式会社 モータシステム、回胴式遊技機、制御方法及びプログラム

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
EIICHI MURAKAWA: "Welding Deformation Simulation Targeting Real Structures", JOURNAL OF HIGH TEMPERATURE SOCIETY OF JAPAN, no. 3, 2007, pages 128 - 136
See also references of EP4306946A4

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2023142321A (ja) * 2022-03-24 2023-10-05 株式会社構造計画研究所 シミュレーション装置、シミュレーション方法及びプログラム

Also Published As

Publication number Publication date
EP4306946A1 (en) 2024-01-17
EP4306946A4 (en) 2025-03-19
US20240160809A1 (en) 2024-05-16
JPWO2022191199A1 (https=) 2022-09-15
JP7488523B2 (ja) 2024-05-22

Similar Documents

Publication Publication Date Title
CN111721787B (zh) 基于晶体塑性的疲劳裂纹萌生与扩展的损伤寿命评估方法
JP7027536B2 (ja) 解析システム及び解析方法
JP4854586B2 (ja) 光学素子のプレス成形シミュレーション方法及びプログラム
Chen et al. An inverse problem in estimating the space-dependent thermal conductivity of a functionally graded hollow cylinder
US20230201927A1 (en) Model predictive control (mpc) for estimating internal temperature distributions within parts being manufactured via the powder bed fusion process
Bartošák et al. Thermo-mechanical fatigue of SiMo 4.06 turbocharger turbine housing: Damage operator approach
Chen et al. Validation of numerical simulations with X-ray diffraction measurements of residual stress in butt-welded steel plates
Seifert et al. TMF life prediction of high temperature components made of cast iron HiSiMo: part II: multiaxial implementation and component assessment
Zhang et al. A quantile-based sequential approach to reliability-based design optimization via error-controlled adaptive Kriging with independent constraint boundary sampling
Ohno et al. Resetting scheme for plastic strain surface in constitutive modeling of cyclic plasticity
JP7488523B2 (ja) 物理定数の推定値取得方法
US11498131B2 (en) Estimating internal temperature distributions within parts being manufactured via the powder bed fusion process
Zhang et al. Contact heat transfer analysis between mechanical surfaces based on reverse engineering and FEM
Mo et al. Machine learning-driven design of support structures and process parameters in additive manufacturing
CN117161824A (zh) 主轴热误差预测方法、装置、设备及存储介质
Woolley et al. Thermocouple data in the inverse heat conduction problem
CN114647914A (zh) 蠕变曲线构造及参数拟合方法和计算机设备
Xu et al. Gleeble-based Johnson–Cook parametric identification of AISI 9310 steel empowered by computational intelligence
Ikushima et al. Development of idealized explicit FEM using GPU parallelization and its application to large-scale analysis of residual stress of multi-pass welded pipe joint
JP5399938B2 (ja) 推定用多項式生成装置、推定装置、推定用多項式生成方法および推定方法
Asadi et al. Analysis of predicted residual stress in a weld and comparison with experimental data using regression model
Spradlin et al. Experimental validation of simulated fatigue life estimates in laser‐peened aluminum
Liu et al. Probabilistic remaining creep life assessment for gas turbine components under varying operating conditions
Teixeira et al. Semi-empirical based response surface approach for reliability evaluation of steel plates with random fields of corrosion
Manjhi et al. Conduction-based standardization of K-type coaxial thermocouple for short-duration transient heat flux measurement

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22767153

Country of ref document: EP

Kind code of ref document: A1

DPE1 Request for preliminary examination filed after expiration of 19th month from priority date (pct application filed from 20040101)
WWE Wipo information: entry into national phase

Ref document number: 2023505582

Country of ref document: JP

WWE Wipo information: entry into national phase

Ref document number: 18281060

Country of ref document: US

WWE Wipo information: entry into national phase

Ref document number: 2022767153

Country of ref document: EP

NENP Non-entry into the national phase

Ref country code: DE

ENP Entry into the national phase

Ref document number: 2022767153

Country of ref document: EP

Effective date: 20231009