CN112528569A - Industrial heating furnace temperature field prediction method based on digital twinning - Google Patents
Industrial heating furnace temperature field prediction method based on digital twinning Download PDFInfo
- Publication number
- CN112528569A CN112528569A CN202011113821.8A CN202011113821A CN112528569A CN 112528569 A CN112528569 A CN 112528569A CN 202011113821 A CN202011113821 A CN 202011113821A CN 112528569 A CN112528569 A CN 112528569A
- Authority
- CN
- China
- Prior art keywords
- temperature
- temperature field
- furnace
- data
- heating furnace
- 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.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/08—Fluids
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Fluid Mechanics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Computing Systems (AREA)
- Pure & Applied Mathematics (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Algebra (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
A temperature field prediction method of an industrial heating furnace based on digital twinning comprises the following steps: establishing a data set of the industrial heating furnace, wherein the data set comprises working condition data and a temperature field determined according to the CFD simulation temperature field; and (3) carrying out data processing: normalizing the data of the working conditions, and reducing a furnace plane temperature cloud chart according to a temperature field; improving the loss function of the cGAN network; training an improved cGAN network; after the network training is finished, a generator which takes the working condition as input and the temperature matrix as output is obtained, and then the working condition data of different planes are respectively input into the generator, and finally the temperature field of the industrial heating furnace is obtained. The invention greatly saves the time and space consumption in the calculation process and can obtain a real-time temperature field. Meanwhile, the mapping model obtained by deep learning has better migration capability, so that more and faster data sources are provided for many research scenes.
Description
Technical Field
The invention relates to a method for predicting a temperature field of an industrial heating furnace. In particular to a temperature field prediction method of an industrial heating furnace based on digital twinning.
Background
An industrial heating furnace is an important device of an industrial refining device, and whether the industrial heating furnace is operated safely or not directly influences the service life, the production capacity and the economic benefit of the device. The combustion process of an industrial heating furnace is unstable, and local overtemperature can occur at random positions during operation. However, the furnace tube of the industrial heating furnace is mostly composed of a process medium which is easy to coke, if a certain local position of the heating furnace runs in an overtemperature state for a long time, the loss and the damage of the furnace tube can be caused, so that measures must be taken to optimize the combustion condition of the temperature field of the heating furnace. However, the heating furnace is large in equipment and severe in environment, so that the related physical quantity parameters are difficult to measure on line, the combustion adjustment cannot be reliably based, the combustion optimization operation is difficult to realize, and only a digital twin model of the temperature field of the industrial heating furnace can be established.
The digital twin model fully utilizes a physical model, sensor data, operation historical data and the like, integrates multidisciplinary, multi-physical quantity and multi-scale simulation processes, and finishes mapping on a physical entity in a virtual space, so that the operation process of the physical entity is reflected.
CFD is an abbreviation for Computational Fluid Dynamics (CFD), which is also one of the digital twin models. CFD solves problems related to fluid flow using methods of numerical analysis. The working condition data of the temperature, the flow and the like of the industrial heating furnace can be used as the basis for CFD calculation, so that the temperature calculation values of a series of points in the industrial heating furnace can be obtained. The CFD can calculate the three-dimensional temperature field data of the industrial heating furnace which is very detailed and abundant. However, the CFD has a large calculation amount and slow convergence, and a real-time temperature field is difficult to obtain.
The Deep learning-based modeling method also belongs to a digital twin model, is applied to a Deep Neural Network (DNN for short), and has strong approximation and convergence capabilities. DNN shows very good performance for complex nonlinear processes in industrial processes. However, DNN is completely data driven and its performance depends on the richness of the data set.
Therefore, researchers have proposed a combination of CFD and DNN, that is, a finite simulated temperature field is calculated by using CFD, and data expansion is performed, and then the simulated temperature field is trained as a training tag. cGAN is an improved version of GAN (generative adaptive nets, generating countermeasure networks). The GAN includes two parts, a generator and a discriminator, wherein the generator generates specific data by using input random noise, and the discriminator is used for judging whether the input data is true or false, namely distinguishing the input data as output data of the generator or real data. The generator's optimization aims at generating data distribution closer to the real data, making it indistinguishable by the arbiter. The optimization goal of the discriminator is to enhance the discrimination ability and discriminate the generated data from the real data as much as possible. Along with the increase of the network iteration times, the generator and the discriminator mutually resist and compete, and finally the generator and the discriminator with better effect are obtained. The cGAN adds condition variables into the input of the generator and the discriminator to perform condition constraint on the generation of data, so that the generation of the data is more directional.
Disclosure of Invention
The invention aims to solve the technical problem of providing a temperature field prediction method of an industrial heating furnace based on digital twinning.
The technical scheme adopted by the invention is as follows: a temperature field prediction method of an industrial heating furnace based on digital twinning comprises the following steps:
1) establishing a data set of the industrial heating furnace, wherein the data set comprises working condition data and a temperature field determined according to the CFD simulation temperature field;
2) performing data processing, including: normalizing the data of the working conditions, and reducing a furnace plane temperature cloud chart according to a temperature field;
3) and improving the cGAN network, and changing the loss function of the cGAN network into:
wherein the content of the first and second substances,
in the formula, x represents an input working condition, y represents a real temperature field, G (-) represents a generator, D (-) represents a discriminator, delta is a self-defined parameter in a Huber Loss function, and lambda is a self-defined weight value;
4) training an improved cGAN network;
5) after the network training is finished, a generator which takes the working condition as input and the temperature matrix as output is obtained, and then the working condition data of different planes are respectively input into the generator, and finally the temperature field of the industrial heating furnace is obtained.
According to the method for predicting the temperature field of the industrial heating furnace based on the digital twin, after the aid of a deep learning network, limited data obtained by CFD are expanded into enough training data, the internal mapping relation between working condition data and temperature field data is found and learned by utilizing the computing power of a computer, a proper mapping model is established, the working condition data is input on the trained deep network model, the three-dimensional temperature field data of the industrial heating furnace with better quality can be obtained quickly, the time and space consumption in the computing process is greatly saved, and a real-time temperature field can be obtained. Meanwhile, the mapping model obtained by deep learning has better migration capability, so that more and faster data sources are provided for many research scenes.
Detailed Description
The digital twin-based industrial heating furnace temperature field prediction method of the present invention is described in detail below with reference to the following examples and the accompanying drawings.
The invention discloses a method for predicting a temperature field of an industrial heating furnace based on digital twinning, which comprises the following steps of:
1) establishing a data set of the industrial heating furnace, wherein the data set comprises working condition data and a temperature field determined according to the CFD simulation temperature field; the method comprises the following steps:
(1.1) operating condition data
The working condition data is used as network input and consists of values obtained by measuring the sensors of the distributed control system DCS in the industrial heating furnace at fixed intervals, such as temperature, pressure, oxygen content, air flow and the like. Since the temperature of the furnace changes relatively slowly, with temperature changes within 1% between one second and 10 minutes, the study uses data at one hour intervals. As shown in table 1, the method specifically includes:
the method comprises the following steps of (1) furnace radiation top pressure (Pa), fuel gas coming temperature (DEG C) from a system, fuel gas coming flow (t/h) from a system pipe network, air flow (t/h), furnace hot air temperature (DEG C) before entering a furnace, radiation feeding to furnace temperature (DEG C), first path radiation feeding flow (t/h) of the furnace, furnace radiation feeding outlet temperature (DEG C) and furnace radiation feeding outlet temperature (DEG C);
TABLE 1
| Description of the invention | Unit of |
| F-101B radiation top pressure | Pa |
| Fuel gas system-derived temperature | ℃ |
| Flow of fuel gas from system pipe network | t/h |
| Air flow rate | t/h |
| F101B hot air temperature before entering furnace | ℃ |
| Radiation feed to F-101B temperature | ℃ |
| First path radiation feed flow of F-101B | t/h |
| F-101B radiation feed exit temperature | ℃ |
| F-101B radiation feed exit temperature | ℃ |
(1.2) CFD simulation temperature field
Because the temperature field has certain stability, the calculated amount can be reduced by clustering the working conditions in the heating furnace according to the change of the working conditions. And dividing different time periods according to the change condition of the working conditions, classifying the time periods with similar working conditions into the same class, realizing the classification of the whole working conditions, and calculating a plurality of CFD simulation temperature fields for each class according to the classification result. The specific process of CFD simulation comprises the following steps:
(1.2.1) selecting a physical model according to the internal structure of the heating furnace: a method for selecting a Reynolds average Navier-Stokes equation (RANS) in turbulence simulation is adopted, a standard k-epsilon model in a turbulence two-equation model is adopted, and when the condition that an object is the combustion of gas fuel in a radiant furnace is involved, a component transportation model related to a volume chemical reaction is also adopted;
(1.2.2) computing the domain and boundary conditions: the boundary conditions of the fuel inlet and the air inlet are mass flow, the boundary condition of the outlet is static pressure, and the wall surface boundary condition is set as the wall surface boundary condition;
(1.2.3) carrying out meshing by adopting a structured grid and an unstructured grid: dividing a cuboid space inside the heating furnace and outside the furnace tubes as a transition region, wherein the transition region can wrap all the furnace tubes; the furnace region except the transition region is named as a main body region; structured grids are used for dividing in a main body area, unstructured grids are used in a furnace tube, a transition area is filled with the unstructured grids, and the total number of the grids is 900-1100 ten thousand;
(1.2.4) CFD simulation calculation: after the grids are divided, the related differential equation set is solved by a numerical analysis method by means of strong calculation power of a computer, the three-dimensional space distribution of the temperature field in the heating furnace is obtained, and the temperature field is confirmed to be a real temperature field.
2) Performing data processing, including: normalizing the data of the working conditions, and reducing a furnace plane temperature cloud chart according to a temperature field; wherein the following steps:
(2.1) carrying out normalization processing on each data of the working condition by adopting the following formula:
wherein, x is data to be normalized, mean, max and min are respectively the mean, the maximum and the minimum in the range of the working condition data training sample;
(2.2) reducing the hearth plane temperature cloud chart according to the real temperature field
After the temperature field is determined, a required plane temperature cloud picture is extracted, the real temperature field is formed by each calculation node of the heating furnace model and comprises three-dimensional coordinates and calculation temperature values of each node, and the three-dimensional coordinates and the calculation temperature values are required to be extracted to restore the furnace plane temperature cloud picture as shown in table 2. However, because the internal structure of the heating furnace is complex, the distribution of the calculation nodes on the space is not uniform, and the calculation nodes cannot be directly used as a temperature matrix. Therefore, points with the same Z coordinate value in the calculation nodes are selected, planar temperature data distribution corresponding to the same Z coordinate value is obtained, a triangular-based cubic interpolation method is utilized, the calculation node dot matrix with uneven spatial distribution is converted into a temperature matrix with even spatial distribution and 68 x 420, and then an isotherm graph is drawn according to the temperature matrix.
TABLE 2
| Node numbering | X axis coordinate | Y-axis coordinate | Z-axis coordinate | Temperature of |
| 1 | 16.6835 | 1.5470 | 5.6668 | 653.4119 |
| 2 | 16.6680 | 1.5635 | 5.6481 | 653.9653 |
| 3 | 16.6655 | 1.5361 | 5.6654 | 653.3988 |
| 4 | 16.6680 | 1.5350 | 5.6481 | 653.6810 |
| 5 | 0.0281 | 1.4843 | 0.6517 | 894.0535 |
| 6 | 0.0257 | 1.5083 | 0.6523 | 838.3954 |
| 7 | 0.0274 | 11.4887 | 0.6205 | 891.9551 |
3) The method for improving the cGAN network changes the loss function of the cGAN network into the following steps:
wherein the content of the first and second substances,
in the formula, x represents an input working condition, y represents a real temperature field, G (-) represents a generator, D (-) represents a discriminator, delta is a self-defined parameter in a Huber Loss function, and lambda is a self-defined weight value;
4) training an improved cGAN network; the method comprises the steps of using each datum of the working condition after normalization processing as an input of an improved cGAN network, using a real temperature matrix after normalization as a truth diagram of the improved cGAN network, training the improved cGAN network, and obtaining a generated temperature matrix.
5) After the network training is finished, a generator which takes the working condition as input and the temperature matrix as output is obtained, and then the working condition data of different planes are respectively input into the generator, and finally the temperature field of the industrial heating furnace is obtained.
Claims (4)
1. A temperature field prediction method of an industrial heating furnace based on digital twinning is characterized by comprising the following steps:
1) establishing a data set of the industrial heating furnace, wherein the data set comprises working condition data and a temperature field determined according to the CFD simulation temperature field;
2) performing data processing, including: normalizing the data of the working conditions, and reducing a furnace plane temperature cloud chart according to a temperature field;
3) and improving the cGAN network, and changing the loss function of the cGAN network into:
wherein the content of the first and second substances,
in the formula, x represents an input working condition, y represents a real temperature field, G (-) represents a generator, D (-) represents a discriminator, delta is a self-defined parameter in a Huber Loss function, and lambda is a self-defined weight value;
4) training an improved cGAN network;
5) after the network training is finished, a generator which takes the working condition as input and the temperature matrix as output is obtained, and then the working condition data of different planes are respectively input into the generator, and finally the temperature field of the industrial heating furnace is obtained.
2. The method for predicting the temperature field of the industrial heating furnace based on the digital twin as claimed in claim 1, wherein the data set of the step 1) comprises:
(1.1) the working condition data specifically comprises:
the method comprises the following steps of (1) furnace radiation top pressure (Pa), fuel gas coming temperature (DEG C) from a system, fuel gas coming flow (t/h) from a system pipe network, air flow (t/h), furnace hot air temperature (DEG C) before entering a furnace, radiation feeding to furnace temperature (DEG C), first path radiation feeding flow (t/h) of the furnace, furnace radiation feeding outlet temperature (DEG C) and furnace radiation feeding outlet temperature (DEG C);
(1.2) a CFD simulated temperature field comprising:
(1.2.1) selecting a physical model according to the internal structure of the heating furnace: a method for selecting a Reynolds average Navier-Stokes equation through turbulence simulation adopts a standard k-epsilon model in a turbulence two-equation model, and also adopts a component transport model related to volume chemical reaction when a condition that an object is burnt by gas fuel in a radiant furnace is involved;
(1.2.2) computing the domain and boundary conditions: the boundary conditions of the fuel inlet and the air inlet are mass flow, the boundary condition of the outlet is static pressure, and the wall surface boundary condition is set as the wall surface boundary condition;
(1.2.3) carrying out meshing by adopting a structured grid and an unstructured grid: dividing a cuboid space inside the heating furnace and outside the furnace tubes as a transition region, wherein the transition region can wrap all the furnace tubes; the furnace region except the transition region is named as a main body region; structured grids are used for dividing in a main body area, unstructured grids are used in a furnace tube, a transition area is filled with the unstructured grids, and the total number of the grids is 900-1100 ten thousand;
(1.2.4) CFD simulation calculation: after the grid division, the three-dimensional space distribution of the temperature field in the heating furnace is obtained by solving the related differential equation system by using a numerical analysis method through a computer, and the temperature field is confirmed to be a real temperature field.
3. The method for predicting the temperature field of the industrial heating furnace based on the digital twin as claimed in claim 1, wherein the step 2) comprises the following steps:
(2.1) carrying out normalization processing on each data of the working condition by adopting the following formula:
wherein, x is data to be normalized, mean, max and min are respectively the mean, the maximum and the minimum in the range of the working condition data training sample;
(2.2) reducing the hearth plane temperature cloud chart according to the real temperature field
The real temperature field is formed by each calculation node of the heating furnace model, comprises three-dimensional coordinates and calculation temperature values of each node, selects points with the same Z coordinate value in the calculation nodes to obtain planar temperature data distribution corresponding to the same Z coordinate value, converts a calculation node lattice with uneven spatial distribution into a temperature matrix with even spatial distribution and the size of 68 x 420 by utilizing a triangle-based cubic interpolation method, and then draws an isotherm graph according to the temperature matrix.
4. The method for predicting the temperature field of the industrial heating furnace based on the digital twin as claimed in claim 1, wherein the step 4) comprises the following steps: and training the improved cGAN network by using the data of the working condition after the normalization processing as the input of the improved cGAN network and using the normalized real temperature matrix as a truth map of the improved cGAN network to obtain a generated temperature matrix.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011113821.8A CN112528569A (en) | 2020-10-17 | 2020-10-17 | Industrial heating furnace temperature field prediction method based on digital twinning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011113821.8A CN112528569A (en) | 2020-10-17 | 2020-10-17 | Industrial heating furnace temperature field prediction method based on digital twinning |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112528569A true CN112528569A (en) | 2021-03-19 |
Family
ID=74978924
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011113821.8A Pending CN112528569A (en) | 2020-10-17 | 2020-10-17 | Industrial heating furnace temperature field prediction method based on digital twinning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112528569A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114036861A (en) * | 2021-10-25 | 2022-02-11 | 中国石油化工股份有限公司 | Three-dimensional temperature field prediction method based on infoGAN |
| CN114117954A (en) * | 2021-11-08 | 2022-03-01 | 北京工业大学 | Dynamic real-time visualization method for three-dimensional reaction field in reactor |
| CN114264373A (en) * | 2021-12-27 | 2022-04-01 | 淮阴工学院 | Method and system for temperature compensation and measured distance enhancement of infrared thermometer |
| CN114722732A (en) * | 2022-06-09 | 2022-07-08 | 华中科技大学 | Method for predicting temperature field of fuel tank of hypersonic aircraft based on point cloud network |
| CN115034138A (en) * | 2022-06-24 | 2022-09-09 | 华北电力大学 | Boiler temperature field prediction method combining computational fluid dynamics and deep learning |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104361156A (en) * | 2014-10-29 | 2015-02-18 | 中国石油化工股份有限公司 | Combustion optimization critical condition determining method on basis of heating furnace combustion model |
| CN109063367A (en) * | 2018-08-22 | 2018-12-21 | 深圳市瑞思物流有限公司 | A kind of cold-storage space optimization method and system based on CFD |
| CN110737935A (en) * | 2019-09-18 | 2020-01-31 | 同济大学 | indoor thermal environment modeling method based on digital twins |
| CN110909508A (en) * | 2019-10-29 | 2020-03-24 | 中国石油化工股份有限公司 | Heating furnace temperature field real-time prediction method based on convolution long and short term memory network |
| CN111460617A (en) * | 2020-03-03 | 2020-07-28 | 华中科技大学 | IGBT junction temperature prediction method based on neural network |
| EP3696622A1 (en) * | 2019-02-14 | 2020-08-19 | Rockwell Automation Technologies, Inc. | Ai extensions and intelligent model validation for an industrial digital twin |
-
2020
- 2020-10-17 CN CN202011113821.8A patent/CN112528569A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104361156A (en) * | 2014-10-29 | 2015-02-18 | 中国石油化工股份有限公司 | Combustion optimization critical condition determining method on basis of heating furnace combustion model |
| CN109063367A (en) * | 2018-08-22 | 2018-12-21 | 深圳市瑞思物流有限公司 | A kind of cold-storage space optimization method and system based on CFD |
| EP3696622A1 (en) * | 2019-02-14 | 2020-08-19 | Rockwell Automation Technologies, Inc. | Ai extensions and intelligent model validation for an industrial digital twin |
| CN110737935A (en) * | 2019-09-18 | 2020-01-31 | 同济大学 | indoor thermal environment modeling method based on digital twins |
| CN110909508A (en) * | 2019-10-29 | 2020-03-24 | 中国石油化工股份有限公司 | Heating furnace temperature field real-time prediction method based on convolution long and short term memory network |
| CN111460617A (en) * | 2020-03-03 | 2020-07-28 | 华中科技大学 | IGBT junction temperature prediction method based on neural network |
Non-Patent Citations (2)
| Title |
|---|
| 李小龙: ""基于数字孪生的机床加工过程虚拟监控系统研究与实现"", 《中国硕士学位论文全文数据库》 * |
| 褚睿峰: ""加热炉内燃烧传热及炉管热流固耦合数值模拟研究"", 《中国硕士学位论文全文数据库》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114036861A (en) * | 2021-10-25 | 2022-02-11 | 中国石油化工股份有限公司 | Three-dimensional temperature field prediction method based on infoGAN |
| CN114036861B (en) * | 2021-10-25 | 2022-09-27 | 中国石油化工股份有限公司 | Three-dimensional temperature field prediction method based on infoGAN |
| CN114117954A (en) * | 2021-11-08 | 2022-03-01 | 北京工业大学 | Dynamic real-time visualization method for three-dimensional reaction field in reactor |
| CN114117954B (en) * | 2021-11-08 | 2023-04-18 | 北京工业大学 | Dynamic real-time visualization method for three-dimensional reaction field in reactor |
| CN114264373A (en) * | 2021-12-27 | 2022-04-01 | 淮阴工学院 | Method and system for temperature compensation and measured distance enhancement of infrared thermometer |
| CN114722732A (en) * | 2022-06-09 | 2022-07-08 | 华中科技大学 | Method for predicting temperature field of fuel tank of hypersonic aircraft based on point cloud network |
| CN115034138A (en) * | 2022-06-24 | 2022-09-09 | 华北电力大学 | Boiler temperature field prediction method combining computational fluid dynamics and deep learning |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112528569A (en) | Industrial heating furnace temperature field prediction method based on digital twinning | |
| CN111400934B (en) | Method for simulating oxygen content distribution of low-temperature carbonization furnace based on WORKBENCH | |
| CN106022377A (en) | Online prediction method for iron ore sintering bed permeability states | |
| CN114036861B (en) | Three-dimensional temperature field prediction method based on infoGAN | |
| CN109460629A (en) | A kind of cooling fan performance optimization method based on approximate model method | |
| CN111898212B (en) | Impeller mechanical profile design optimization method based on BezierGAN and Bayesian optimization | |
| CN110750855B (en) | Volute molded line design method under shape sizing limitation | |
| CN111069328A (en) | Isothermal extrusion process parameter optimization method based on particle swarm optimization | |
| CN104750948A (en) | Optimization method for processing multiple extreme values and multiple restricted problems in flight vehicle design | |
| CN114780909A (en) | Partial differential equation solving method and system based on physical information neural network | |
| Li et al. | Thermal error modeling of electrical spindle based on optimized ELM with marine predator algorithm | |
| CN115186555A (en) | Drying equipment live simulation method based on digital twin and related equipment | |
| CN113158355B (en) | All-condition optimization design method for low-temperature liquid expander | |
| Guo et al. | Data mining and application of ship impact spectrum acceleration based on PNN neural network | |
| CN115481510B (en) | Radial compressor blade multi-working-condition optimization method and device based on improved NSGA-II algorithm | |
| CN110728072A (en) | Method for determining computational fluid mechanics analysis grid size of digital reactor | |
| CN105930936A (en) | Growth prediction method of microorganisms included in food in production circulation process | |
| Miao et al. | Local modified mesh deformation based on radial basis functions for fluid solid interaction in reactor core | |
| CN112580692B (en) | Virtual sample generation method based on interpolation algorithm | |
| EP2442279B1 (en) | Design optimization of physical bodies | |
| Lee et al. | Development of a web-based open source CAE platform for simulation of IC engines | |
| CN110688805A (en) | Analysis method for flow numerical value of automobile external flow field | |
| CN111860923A (en) | Boiler flue gas NO based on multi-model clustering integrationxEmission prediction algorithm | |
| Cao et al. | Efficient optimization design of flue deflectors through parametric surrogate modeling with physics-informed neural networks | |
| CN114781285B (en) | Biomass large particle pyrolysis simulation method based on sphere cluster hypothesis and Laguerre-Voronoi structure |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210319 |