Method for determining wall heat load in rocket substage recovery process

Abstract

The application provides a method for determining wall heat load in a rocket substage recovery process, and relates to the technical field of wall heat load prediction, comprising the following steps: obtaining target working condition parameters of a rocket substage in a recovery process and target basic heat flow distribution data of an outer wall surface of the rocket substage; processing the target basic heat flow distribution data to obtain a basic heat flow feature; from a pre-constructed sample working condition library, screening a plurality of sample working condition parameters associated with the target working condition parameters and a feature error field corresponding to the respective sample working condition parameters; and determining the wall heat load of the rocket substage in the recovery process based on the plurality of feature error fields, the basic heat flow feature and the target basic heat flow distribution data. The application can realize the rapid, stable and interpretable determination of the wall heat load of the rocket substage in the recovery process under the premise of ensuring the prediction reliability.

Description

Technical Field

[0001] This application relates to the field of wall thermal load prediction technology, and more specifically, to a method for determining wall thermal load during the recovery of a rocket's first stage. Background Technology

[0002] Existing methods for rapid prediction of wall thermal loads during rocket recovery rely on black-box prediction methods (such as deep neural networks, support vector regression, and random forests). These methods pre-obtain wall heat flux distribution data under several sample operating conditions through numerical simulations or ground / flight tests, and extract operating parameters (such as flight altitude, incoming Mach number, total jet pressure, and rocket body configuration parameters) as input features to establish an end-to-end nonlinear mapping model from operating parameters to wall heat flux distribution. After training, for any target operating condition, only its operating parameters need to be input, and the model can directly output the corresponding wall heat flux distribution without repeatedly calling the computational fluid dynamics (CFD) solver. However, the wall thermal loads predicted by these methods are inaccurate. Summary of the Invention

[0003] The purpose of this application is to provide a method for determining the wall thermal load during the recovery of a rocket's first stage, thereby solving the aforementioned problems in the prior art and obtaining the wall thermal load during the recovery of a rocket's first stage with high accuracy.

[0004] Firstly, a method for determining the wall thermal load during the recovery of a rocket's first stage is provided, the method including: Acquire target operating parameters of the rocket's first stage during the recovery process and target basic heat flow distribution data of the rocket's first stage outer wall surface; The target basic heat flux distribution data are processed to obtain basic heat flux characteristics; From a pre-built sample operating condition library, multiple sample operating condition parameters associated with the target operating condition parameters and the corresponding feature error fields are selected. Based on multiple characteristic error fields, the basic heat flow characteristics, and the target basic heat flow distribution data, the wall thermal load of the rocket's first stage during the recovery process is determined.

[0005] In an optional implementation, the method for obtaining the target basic heat flux distribution data includes: Based on the target operating condition parameters, a basic heat flow calculation model for the target operating condition is constructed; The basic heat flow calculation model was used to perform fluid dynamics simulation on the external flow field and jet coupled flow field of the first stage of the rocket under the target operating conditions, and the basic heat flow distribution data of the outer wall of the first stage of the rocket was obtained. The basic heat flux distribution data is processed to obtain the target basic heat flux distribution data.

[0006] In an optional implementation, the basic heat flux distribution data includes: different wall coordinates and the wall heat flux density corresponding to each wall coordinate; The basic heat flux distribution data is processed to obtain the target basic heat flux distribution data, including: For any wall coordinate in the basic heat flow distribution data, determine the region of the outer wall of the first stage of the rocket to which the wall coordinate belongs; For any given region, based on the pre-configured mapping parameters for that region, coordinate system transformation and mapping processing are performed on the basic heat flow distribution data of each wall coordinate within that region to obtain the target basic heat flow data of each wall coordinate.

[0007] In an optional implementation, the target basic heat flux distribution data is processed to obtain basic heat flux characteristics, including: For any region on the outer wall of the first stage of a rocket, feature analysis is performed on the target basic heat flow data of all wall coordinates within the region to obtain the basic heat flow characteristics of the region; wherein, the basic heat flow characteristics include: intensity characteristics, range characteristics, location characteristics and distribution characteristics.

[0008] In an optional implementation, the sample operating condition library contains different sample operating condition parameters, sample operating condition feature vectors corresponding to the corresponding sample operating condition parameters, and basic heat flow characteristics, reference heat flow characteristics, and feature error fields for each region; Based on multiple characteristic error fields, the basic heat flow characteristics, and the target basic heat flow distribution data, the wall thermal load of the rocket's first stage during recovery is determined, including: The target operating condition parameters are processed to obtain the target operating condition feature vector; Calculate the correlation weight between the target working condition feature vector and the sample working condition feature vectors corresponding to the selected multiple feature error fields; For any region on the outer wall of the first stage of the rocket, the feature error field of each sample feature vector in the region is reconstructed by using the correlation weight between the target feature vector and each sample feature vector, so as to obtain the target feature error field of the region. Based on the target characteristic error field, basic heat flow characteristics, and target basic heat flow distribution data of different regions, the wall thermal load of the rocket's first stage during the recovery process is determined.

[0009] In an optional implementation, based on the target characteristic error field, basic heat flux characteristics, and target basic heat flux distribution data for each region, the wall thermal load of the rocket's first stage during recovery is determined, including: For any region on the outer wall of the first stage of a rocket, the basic heat flow characteristics of the region are used to correct the target feature error field of the region, so as to obtain the corrected target feature error field of the region. Based on the corrected target feature error fields of the region and the target basic heat flow distribution data of the region, the wall heat load of the region is generated. Based on the wall thermal load of each region, the wall thermal load of the first stage of the rocket during the recovery process is obtained.

[0010] In an optional implementation, the wall thermal load of the region is generated based on the corrected target feature error fields of the region and the target basic heat flux distribution data of the region, including: The wall thermal load of the region is obtained by superimposing the corrected target feature error fields of the region with the target basic heat flow distribution data of the region.

[0011] Secondly, a device for determining the wall thermal load during the recovery of a rocket's first stage is provided, the device comprising: The acquisition unit is used to acquire the target operating parameters of the first stage of the rocket during the recovery process and the target basic heat flow distribution data of the outer wall of the first stage of the rocket. The processing unit is used to process the target basic heat flow distribution data to obtain basic heat flow characteristics; The filtering unit is used to filter multiple sample operating parameters associated with the target operating parameter and the corresponding feature error fields from a pre-built sample operating condition library. The determination unit is used to determine the wall thermal load of the rocket's first stage during the recovery process based on multiple characteristic error fields, the basic heat flow characteristics, and the target basic heat flow distribution data.

[0012] Thirdly, an electronic device is provided, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in memory, it implements any of the steps described in the first aspect above.

[0013] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements the steps of any of the methods described in the first aspect above.

[0014] This application obtains target operating condition parameters and basic heat flux distribution data of the outer wall surface during the rocket's first-stage recovery process, extracts basic heat flux characteristics from these data, and combines them with the characteristic error fields corresponding to multiple sample operating conditions associated with the current operating condition selected from a pre-built sample operating condition library to ultimately determine the wall thermal load of the target operating condition. It eliminates the need for repeated high-precision reference heat flux models to perform complete CFD simulations for each target operating condition. Instead, it uses prior information about the error field contained in the sample operating conditions for correction and reconstruction based on the rapid calculation results of the basic heat flux model, significantly reducing the computational cost and time cycle for predicting wall thermal loads. Simultaneously, the constraints of target operating condition parameter association and basic heat flux characteristics help improve the physical consistency and stability of error field migration, avoiding the uncertainties of pure data-driven black-box methods when samples are sparse or operating condition extrapolation occurs. Therefore, this application can achieve rapid, stable, and interpretable determination of wall thermal loads during the rocket's first-stage recovery process while ensuring prediction reliability, effectively supporting multi-condition thermal environment assessment and iterative thermal protection design. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating a method for determining the wall thermal load during the recovery of a rocket's first stage, provided in an embodiment of this application; Figure 2 A schematic diagram of a device for determining the wall thermal load during the recovery process of a rocket first stage, provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by those skilled in the art. The words "first," "second," and similar terms used in this application do not indicate any order, quantity, or importance, but are only used to distinguish different components. The words "comprising" or "including," etc., mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, but do not exclude other elements or objects. The words "connected," "coupled," or "connected," etc., are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up," "down," "left," "right," etc., are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0018] The method for determining the wall heat load during the recovery process of the first stage of a rocket, provided in this application embodiment, can be applied to a server or terminal. The server can be a physical server, a server cluster composed of multiple physical servers, or a distributed system. It can also be a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The terminal can be a user equipment (UE) such as a mobile phone, smartphone, laptop, digital radio receiver, personal digital assistant (PDA), or tablet computer (PAD), a handheld device, an in-vehicle device, a wearable device, a computing device, or other processing devices connected to a wireless modem, a mobile station (MS), or a mobile terminal.

[0019] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0020] Figure 1This is a flowchart illustrating a method for determining the wall thermal load during the recovery of a rocket's first stage, as provided in an embodiment of this application. Figure 1 As shown, the method may include: Step S110: Obtain the target operating parameters of the first stage of the rocket during the recovery process and the target basic heat flow distribution data of the outer wall of the first stage of the rocket.

[0021] The target operating parameters may include flight status parameters, jet operating parameters, and rocket body configuration parameters; flight status parameters include flight altitude, incoming flow velocity, incoming flow Mach number, incoming flow density, incoming flow pressure, and incoming flow temperature; jet operating parameters include the number of working nozzles, total jet outlet pressure, total jet outlet temperature, jet mass flow rate, jet velocity, or equivalent parameters characterizing jet intensity; rocket body configuration parameters include rocket body dimensions, nose section shape, base geometry, and nozzle arrangement parameters.

[0022] In specific implementation, a basic heat flux calculation model for the target operating condition is constructed based on the target operating condition parameters. Specifically, a basic heat flux calculation model for the target operating condition is constructed based on the target operating condition parameters and the properties of the configured first fluid material. Specifically, an external flow field coupled with the jet is constructed based on the rocket body configuration parameters. The jet inlet boundary is determined based on the jet operating parameters. The far-field boundary is determined based on the incoming flow parameters. The outer wall surface of the rocket body is used as the wall heat flux extraction object. Based on the first fluid material properties, the external flow field coupled with the jet calculation domain, the jet inlet boundary, the far-field boundary, and the wall heat flux extraction object, a basic heat flux calculation model for the target operating condition is constructed. Among them, the basic heat flux calculation model is used to obtain the main field distribution of wall heat flux, and can be a uniform flow model or a frozen flow model. A basic heat flux calculation model was used to perform fluid dynamics simulation on the external flow field and the coupled flow field of the jet stream of the first stage of the rocket under the target operating conditions, and the basic heat flux distribution data of the outer wall of the first stage of the rocket was obtained. The basic heat flux distribution data may include: the coordinates of different walls of the outer wall of the first stage of the rocket and the coordinate number corresponding to the corresponding wall coordinates and the heat flux density of the first wall; the coordinates of different walls are coordinates in a plane rectangular coordinate system.

[0023] The basic heat flux distribution data is processed to obtain the target basic heat flux distribution data; Specifically, for any wall coordinate in the basic heat flow distribution data, the region of the outer wall surface of the first stage of the rocket to which that wall coordinate belongs is determined. This region can be any one of the nose section, cylindrical wall section, or base section. The outer wall surface of the first stage of the rocket is pre-divided into the nose section, cylindrical wall section, and base section. The nose section is the outer wall surface corresponding to the shape of the nose section of the rocket body. The cylindrical wall section is the main side wall section extending along the rocket body axis after the nose section. The base section is the outer wall surface of the base section at the tail end of the rocket body. Each region corresponds to a range of wall coordinates. If any wall coordinate falls within the range of wall coordinates corresponding to any region, then that wall coordinate belongs to that region. The boundary between the nose section and the cylindrical wall section can be determined by the intersection of the nose section shape and the cylindrical section. The boundary between the cylindrical wall section and the base section can be determined by the intersection of the base plane at the tail end and the cylindrical side wall. In cases where there are local transitional connection regions, they can be merged into the corresponding main regions based on their connection relationship with adjacent main regions, local geometry, and pre-defined partition boundary positions. This ensures that the regional affiliation of each wall position is clear and unique, and that the subsequent parameter coordinate expression remains consistent and smooth after partitioning. For any region on the outer wall of the first stage of a rocket, based on the pre-configured mapping parameters for that region, coordinate system transformation and mapping processing are performed on the basic heat flux distribution data of each wall coordinate within that region to obtain the target basic heat flux data of each wall coordinate within that region. The mapping parameters may include a target coordinate system and a target reference point set. Processing the basic heat flux distribution data may include coordinate system transformation and mapping processing. Mapping processing may employ nearest neighbor mapping, inverse distance weighted interpolation, radial basis interpolation, or resampling methods. The target coordinate system for the cylindrical wall region may be an axial coordinate system and a circumferential coordinate system; the target coordinate system for the base region may be a planar coordinate system or a polar coordinate system; and the target coordinate system for the head region may be surface parametric coordinates or discrete surface local coordinates based on a unified reference point set.

[0024] In the above embodiments of this application, the method for determining the target reference point set may include: directly selecting the wall discrete point set corresponding to a certain heat flow model under a certain preset sample condition as the reference point set; or, based on the rocket body configuration parameters of the first stage of the rocket, resampling in each wall region according to a preset density to generate the reference point set; or, filtering, merging or resampling the original wall discrete point set to form the reference point set. The target reference point sets of different regions are not the same, not only in terms of the number of reference points contained in the target reference point set, but also in terms of the coordinates of the corresponding reference points. The number of reference points contained in the target reference point set of each region is determined according to the preset sampling density of that region. The preset sampling density of different regions is determined according to the changes in wall geometric curvature, local heat flow gradient changes and subsequent error field feature extraction requirements. In regions with large geometric changes, large heat flow gradients or feature-sensitive regions, the reference point sampling density can be appropriately increased so that the heat flow distribution and error distribution of each region can be expressed on a unified point set.

[0025] In another embodiment of this application, based on pre-configured mapping parameters for the region, coordinate system transformation and mapping processing are performed on the basic heat flux distribution data of each wall coordinate within the region to obtain the target basic heat flux data of each wall coordinate within the region. This may include: Transform the coordinates of each wall surface in the region from the basic heat flux distribution data to the target coordinate system to obtain the target wall coordinates corresponding to each wall surface coordinate in the region. Select target reference points from the target reference point set, excluding the target wall coordinates, as supplementary reference points; Using nearest neighbor mapping, inverse distance weighted interpolation, radial basis interpolation, or resampling methods, along with corresponding preset interpolation parameters, the first wall heat flux density of each reference point to be supplemented is determined based on the coordinates of each target wall and the corresponding first wall heat flux density. Inverse distance weighted interpolation is preferred, as it is suitable for local mapping when the distribution of discrete nodes on the wall is inconsistent under different operating conditions and models. It can reconstruct heat flux values ​​based on spatial proximity, thus better preserving the local spatial characteristics of the original heat flux distribution. Furthermore, the implementation process is simple and convenient for engineering applications. When using inverse distance weighted interpolation, for any reference point to be supplemented, the k nearest neighbor target wall coordinates within the neighborhood of the reference point to be supplemented are selected from multiple target wall coordinates. The mapped heat flux value of the reference point to be supplemented can then be expressed as: ;in, Indicates the first One reference point to be added The heat flux density of the first wall surface after mapping; This indicates participation in the m-th reference point to be supplemented. The coordinates of the k-th nearest neighbor target wall and the m-th reference point to be supplemented Spatial distance; K represents the m-th reference point to be supplemented. The number of nearest neighbor target wall coordinates; m and k are both positive integers; This represents the m-th reference point to be supplemented. The coordinates of the k-th nearest neighbor target wall The heat flux density at the first wall surface; Indicates the first One reference point to be added The coordinates of the k-th nearest neighbor target wall interpolation weights, ; Indicates the distance weighting index. This indicates that the denominator should not be a singular small positive number when the distance is zero. The meaning of the above inverse distance weighted interpolation method is that for any reference point in the target reference point set, its mapped heat flux value is obtained by weighted average of the heat flux values ​​of several original wall discrete points in its neighborhood. The closer the original point is to the reference point, the greater its corresponding weight, so that the mapping result can better maintain the local spatial characteristics of the original heat flux distribution. Based on the coordinates of each target wall, each reference point to be supplemented, the region identifier of the corresponding area, and the heat flux density of the first wall, the target basic heat flux data is obtained. Specifically, the coordinates of each reference point to be supplemented are used as the target wall coordinates. Among them, the region identifier is used to represent whether each target wall coordinate and each reference point to be supplemented belongs to the head region, cylindrical wall region, or base region. The region identifier can be implemented by any of the following methods: character identifier, numerical identifier, or enumerated identifier, as long as it can uniquely identify different wall partitions. The target basic heat flux data may include: multiple target wall coordinates, the region identifier of the area to which each target wall coordinate belongs, the coordinate number corresponding to each target wall coordinate, and the heat flux density of the first wall.

[0026] In the above embodiments of this application, the target reference point set may include a first target reference point set and a second target reference point set, wherein the number of reference points contained in the first target reference point set and the second target reference point set is different; the data sampling density of the second target reference point set is greater than the data sampling density of the first target reference point set, and the method may further include: Calculate the extreme value of the first wall heat flux density corresponding to the coordinates of each target wall; if the extreme value is not less than the extreme value threshold corresponding to the region to which the coordinates of each target wall belong, then determine the reference point to be supplemented based on the second target reference point set; if the extreme value is less than the extreme value threshold corresponding to the region to which the coordinates of each target wall belong, then determine the reference point to be supplemented based on the first target reference point set.

[0027] In another embodiment of this application, after determining the first wall heat flux density of each reference point to be supplemented based on the coordinates of each target wall and the first wall heat flux density corresponding to the corresponding target wall coordinates, the method may further include: Based on the target wall coordinates and the corresponding first wall heat flux density, calculate the first characteristic value; wherein, the first characteristic value can be one or more of the following: peak value, mean value, integral value, and main distribution characteristics; the peak value can be defined as the maximum heat flux in the region, the mean value can be defined as the area-weighted average value of the heat flux in the region, the integral value can be defined as the integral value of the heat flux in the region with respect to the area, and the main distribution characteristics can be defined as the average distribution characteristics along the corresponding parameter coordinate direction; Calculate the second characteristic value based on the target wall coordinates, each reference point to be supplemented, and the corresponding first wall heat flux density; If the difference between the first feature value and the second feature value exceeds the configured difference range, the verification fails. The target reference point set and interpolation parameters are adjusted. Based on the adjusted target reference point set and interpolation parameters, the execution steps are returned: Target reference points other than the target wall coordinates are selected from the target reference point set as supplementary reference points; the first wall heat flux density of each supplementary reference point is determined using nearest neighbor mapping, inverse distance weighted interpolation, radial basis interpolation, or resampling methods, based on the target wall coordinates and the corresponding first wall heat flux density, until the first wall heat flux density of each supplementary reference point that passes the verification is obtained. If the difference between the first feature value and the second feature value does not exceed the configured difference range, the verification will pass and no further processing will be performed.

[0028] In another embodiment of this application, transforming the wall coordinates in the basic heat flux distribution data to the target coordinate system to obtain the target wall coordinates corresponding to each wall coordinate may include: When the region is the head region, the generatrix arc length and circumferential angle are preferably used as parameter coordinates. Specifically, if the head generatrix curve is discretized into several curve points, then the generatrix arc length corresponding to any point on the head surface can be expressed as the cumulative curve length along the head generatrix from a preset starting point. The circumferential angle of the corresponding point on the head region is used to characterize the position of that point around the rocket body axis; the generatrix arc length is used to characterize the position of the reference point along the head generatrix direction. Thus, the wall heat flow in the head region can be represented as a surface parameterized distribution with respect to the generatrix arc length and circumferential angle. For head regions with complex geometry and where it is difficult to establish global regular surface parameter coordinates, the target reference point set of the head region can also be directly used as a discrete expression carrier, and local surface coordinates can be constructed through local neighborhood relationships for subsequent comparison of heat flow distribution and error distribution and feature extraction. When the region is a cylindrical wall region, axial and circumferential coordinates are preferably used as parametric coordinates. Preferably, assuming the direction of the rocket body axis is the axial direction, the axial coordinate of any wall coordinate in the cylindrical wall region is used to characterize the position of that wall coordinate along the rocket body axis; the circumferential coordinate of any wall coordinate in the cylindrical wall region is used to characterize the position of that wall coordinate about the rocket body axis; thus, the wall heat flow in the cylindrical wall region can be expressed as a parametric distribution with respect to the axial and circumferential coordinates. When the region is a large base area, planar coordinates or polar coordinates are preferred. When using polar coordinates, for any wall coordinate on the large base area, the radial coordinate represents the position of the reference point relative to the center of the large base, and the angular coordinate represents the directional position of the reference point around the center of the large base. Thus, the wall heat flux in the large base area can be represented as a parameterized distribution with respect to the radial and angular coordinates. When the large base area contains an opening corresponding to the nozzle connection location, the parameterized coordinate expression is established within the effective wall area excluding the opening region.

[0029] Step S120: Process the target basic heat flow distribution data to obtain the basic heat flow characteristics.

[0030] In specific implementation, for any region on the outer wall of the first stage of the rocket, the target basic heat flow data of all wall coordinates within that region are subjected to feature analysis to obtain the basic heat flow characteristics of that region. These basic heat flow characteristics can include: intensity characteristics, range characteristics, location characteristics, and distribution characteristics. Intensity characteristics can include one or more of peak value, mean value, and integral value. Range characteristics can include the proportion of the high-heat area. Location characteristics can include one or more of the heat flow peak location and the location of the main high-heat center of mass. Distribution characteristics can include one or more of the average distribution characteristics and peak distribution characteristics along the wall coordinate direction. Based on the basic heat flow characteristics of each region, the basic heat flow characteristics are obtained.

[0031] In the above embodiments of this application, feature analysis is performed on the target basic heat flow data of all wall coordinates within the region to obtain the basic heat flow characteristics of the region, specifically including: For any region on the outer wall of the first stage of a rocket, calculate the local wall area corresponding to each wall coordinate in the region based on the preset attribute parameters of the region and the coordinates of all wall surfaces in the region. The maximum value of the first wall heat flux density at all wall coordinates within the region is taken as the peak value of the basic heat flux main field of the region. Based on the heat flux density of the first wall surface and the local wall area corresponding to each wall surface coordinate in the region, calculate the mean value of the primary heat flux field and the integral value of the primary heat flux field in the region. Based on the first wall heat flux density of all wall coordinates within the region and the corresponding local wall area, at least one high-heat zone is determined within the region. The range characteristic of the region is obtained by the ratio of the area of ​​all high-heat zones to the area of ​​the region itself. Specifically, if the first wall heat flux density of any wall coordinate within the region is not less than a preset high-heat zone threshold, then that wall coordinate belongs to a high-heat zone. The sum of the local wall areas of all wall coordinates belonging to the high-heat zone is taken as the area of ​​all high-heat zones within the region. The sum of the local wall areas of all wall coordinates within the region is taken as the area of ​​the region. The ratio of the area of ​​all high-heat zones within the region to the area of ​​the region is taken as the high-heat zone area ratio, i.e., the range characteristic of the region. The preset high-heat zone threshold can be determined based on the peak value of the first wall heat flux density within the region and a preset proportionality coefficient. The preset proportionality coefficient can be set according to the requirements of the heat load analysis, for example, it can be 0.8. The wall coordinates corresponding to the maximum first wall heat flux density within the region are taken as the peak heat flux location of the region. Connectivity analysis is performed on each high-heat zone within the region to obtain the connectivity analysis results. Based on the connectivity analysis results and the local wall area and first wall heat flux density corresponding to each wall coordinate within the region, the main high-heat zone is extracted from multiple high-heat zones. The wall coordinates corresponding to the centroid of the main high-heat zone are taken as the centroid location of the main high-heat zone within the region. The peak heat flux location and the centroid location of the main high-heat zone within the region are taken as the location features of the region. The distribution characteristics of the region are obtained by analyzing the first wall heat flux density and local wall area of ​​all wall coordinates within the region. These distribution characteristics are along the parameter coordinate directions. Specifically, when the region is a cylindrical wall region, binning statistics are performed along both axial and circumferential coordinates. The average distribution along the axial direction can be determined based on the first wall heat flux density and local wall area of ​​each axial bin. The average distribution along the circumferential direction can also be determined based on the first wall heat flux density and local wall area of ​​each circumferential bin. When the region is a large base region, binning statistics can be performed along either radial or angular coordinates. The average distribution along the radial direction can be determined based on the first wall heat flux density and local wall area of ​​each radial bin. For the head region, binning statistics can be performed along the generatrix arc length and circumferential directions. The average distribution along the generatrix arc length can be determined based on the first wall heat flux density and local wall area of ​​each generatrix arc length bin, or, further, the peak distribution along the corresponding parameter directions can be extracted.

[0032] In one embodiment of this application, calculating the local wall area corresponding to each wall coordinate within the region based on preset attribute parameters of the region and the coordinates of all walls within the region may include: When the region is a cylindrical wall region, the preset attribute parameters may include: cylinder radius; determining the local coordinate increment corresponding to each wall coordinate in the region; and calculating the local wall area corresponding to each wall coordinate based on the cylinder radius and the local coordinate increment corresponding to each wall coordinate. When the region is a large base region, determine the local coordinate increments corresponding to the coordinates of each wall within the region; calculate the local wall area corresponding to each wall coordinate based on the local coordinate increments corresponding to each wall coordinate and the target coordinate system; if the target coordinate system is a polar coordinate system, the local wall area can be calculated based on the radial position and the local coordinate increments corresponding to each wall coordinate; if the target coordinate system is a planar coordinate system, the local wall area can be calculated based on the local coordinate increments in two planar coordinate directions. When the region is the head region, the preset attribute parameters may include: the coordinates of the rocket body axis; if the generatrix arc length and circumferential angle are used as the wall coordinates, then the local coordinate increments corresponding to each wall coordinate in the region and the radial distance from each wall coordinate to the rocket body axis are determined; based on the local coordinate increments and radial distances corresponding to each wall coordinate, the local wall area corresponding to each wall coordinate is calculated.

[0033] In another embodiment of this application, calculating the local wall area corresponding to each wall coordinate in the region based on preset attribute parameters of the region and the coordinates of all walls in the region may further include: When the coordinates of each wall surface contained within any region are an irregular discrete set of points, the area can be approximately determined based on the local area of ​​the neighborhood of each wall surface coordinate. Preferably, any wall surface coordinate and its neighboring points can be used to form a local triangulation, and the local wall area corresponding to the wall surface coordinate can be approximately determined based on the sum of the areas of the local triangles connected to the wall surface coordinate, or the area after it is evenly distributed by the vertices. Alternatively, the local wall area corresponding to the wall surface coordinate can also be approximately determined based on the area of ​​the Voronoi neighborhood corresponding to the wall surface coordinate.

[0034] In one embodiment of this application, connectivity analysis is performed on each high-heat zone within the region to obtain connectivity analysis results; based on the connectivity analysis results and the local wall area and first wall heat flux density corresponding to the coordinates of each wall in the region, the main high-heat zone is extracted from multiple high-heat zones, which may include: Connectivity is determined based on the adjacency or proximity of the coordinates of each wall surface within the high-heat zone, or if the two high-heat zones are adjacent in the same area, then the two high-heat zones are determined to be connected; or, connectivity is determined based on a preset distance threshold, that is, if the spatial distance or parameter coordinate distance between the coordinates of the walls of two high-heat zones is less than the preset threshold, then the two high-heat zones are determined to be connected. The main high-temperature zone can be extracted from multiple high-temperature zones according to any of the following criteria: the largest area; the largest absolute integral value; the largest peak intensity; the largest combined index based on area, absolute integral value and peak intensity; preferably, the combined index can be determined by weighted summation based on area, absolute integral value and peak intensity; preferably, the high-temperature zone with the largest combined index is selected as the main high-temperature zone.

[0035] Step S130: From the pre-built sample operating condition library, select multiple sample operating condition parameters and the characteristic error fields of the corresponding sample operating condition parameters that are associated with the target operating condition parameter.

[0036] The sample operating condition library contains multiple sample operating conditions, sample operating condition parameters corresponding to different sample operating conditions, sample operating condition feature vectors, basic heat flow characteristics, reference heat flow characteristics, and feature error fields. The feature error field is constructed based on the errors of the basic heat flow characteristics and reference heat flow characteristics of any sample operating condition.

[0037] In practice, all sample operating condition parameters in the sample operating condition library can be used as sample operating condition parameters associated with the target operating condition parameter; alternatively, the association weight between each sample operating condition feature vector and the target operating condition feature vector of the target operating condition parameter can be calculated, and the sample operating condition parameters corresponding to the sample operating condition feature vectors with association weights greater than a preset weight threshold can be used as sample operating condition parameters associated with the target operating condition parameter.

[0038] In one embodiment of this application, the method for constructing the sample operating condition library may include: 1.1 Obtain different sample operating conditions and sample operating condition parameters during the recovery of the first stage of the rocket; process the sample operating condition parameters of each sample operating condition to obtain the sample operating condition feature vector of each sample operating condition. 1.2 For any sample operating condition, based on the sample operating condition parameters and the configured first and second fluid material properties, a basic heat flow calculation model and a reference heat flow calculation model for that sample operating condition are constructed; based on the sample operating condition parameters and the configured first fluid material properties, a basic heat flow calculation model for that sample operating condition is constructed; based on the sample operating condition parameters and the configured second fluid material properties, a reference heat flow calculation model for that sample operating condition is constructed; the reference heat flow calculation model can adopt a multi-component diffusion model or a reactive flow model; this application uses the same geometric object and the same sample operating condition to construct the calculation model under the basic heat flow model and the calculation model under the reference heat flow model for each sample operating condition, so as to ensure the comparability of the subsequently obtained wall heat flow results.

[0039] 1.3 Computational fluid dynamics numerical simulations were performed based on the basic heat flux calculation model and the reference heat flux calculation model to obtain basic heat flux distribution data and reference heat flux distribution data. The basic heat flux distribution data may include: the coordinates of different walls of the outer wall of the first stage of the rocket and the first wall heat flux density corresponding to each wall coordinate. The reference heat flux distribution data may include: the coordinates of different walls of the outer wall of the first stage of the rocket and the second wall heat flux density corresponding to each wall coordinate.

[0040] 1.4 For any given wall coordinates, determine the region on the outer wall of the first stage of the rocket to which those coordinates belong; 1.5 For any region, based on the pre-configured mapping parameters for that region, the basic heat flux distribution data and reference heat flux distribution data of the wall coordinates are processed to obtain the target basic heat flux data and target reference heat flux distribution data of the wall coordinates; 1.6 Feature analysis was performed on the target basic heat flux data and target reference heat flux distribution data of the wall coordinates contained in the region to obtain the basic heat flux characteristics and reference heat flux characteristics of the region under the sample working condition. 1.7 Based on the basic heat flux characteristics and reference heat flux characteristics of the region under the sample operating conditions, determine the characteristic error values ​​of all wall coordinates contained in the region under the sample operating conditions; specifically, for any wall coordinate in any region, the difference between the heat flux density of the second wall and the heat flux density of the first wall is taken as the characteristic error value of the wall coordinate under the sample operating conditions. ;in, This represents the heat flux density at the second wall surface; The heat flux density of the first wall surface is represented; the overall characteristic error field is divided into the head region error field, the cylindrical wall region error field, and the bottom region error field. 1.8 Process the characteristic error values ​​of all wall coordinates contained in the region under the sample working condition to obtain the characteristic error field of the region under the sample working condition; specifically, extract the intensity characteristics, range characteristics, location characteristics, and distribution characteristics of the characteristic error values ​​of all wall coordinates contained in the region under the sample working condition, and combine them with the characteristic error values ​​of all wall coordinates contained in the region under the sample working condition to obtain the characteristic error field of the region under the sample working condition; specifically, the characteristic error field may include: the region identifier of the region, all wall coordinates contained in the region, and the characteristic error values, intensity characteristics, range characteristics, location characteristics, and distribution characteristics of the corresponding wall coordinates; intensity characteristics include one or more of the following: positive peak value, negative peak value, absolute peak value, mean, absolute mean, integral value, and absolute integral value; range characteristics include one or more of the following: high error area ratio, positive error high value area ratio, and negative error high value area ratio; location characteristics include one or more of the following: error peak position, centroid position of the main high error sub-region, and location characteristics of multiple high error sub-regions; distribution characteristics include one or more of the following: average distribution characteristics and peak distribution characteristics along the parameter coordinate direction. 1.9 Based on the sample operating condition parameters, sample operating condition feature vectors, and the basic heat flow characteristics, reference heat flow characteristics, and feature error fields of each region under each sample operating condition, a sample operating condition library is constructed.

[0041] In one embodiment of this application, the intensity features may include: peak features, mean features, absolute mean features, and integral features; the peak features include positive peaks, negative peaks, and absolute peaks; Extracting the intensity, range, location, and distribution characteristics of the feature error values ​​of all wall coordinates within this region under the sample working condition can include: (1) The positive peak value of the characteristic error field in this region can be the maximum value of the characteristic error value in this region; the negative peak value of the characteristic error field in this region can be the minimum value of the characteristic error value in this region; the absolute peak value of the characteristic error field in this region can be the maximum value of the absolute value of the characteristic error value in this region; (2) Mean characteristics: The mean value of the characteristic error field in this region is preferably calculated using the area-weighted average method; (3) Absolute mean characteristic: The absolute mean of the characteristic error field in this region is used to characterize the average deviation intensity of the characteristic error field in this region, and can be calculated based on area weighting; (4) The integral value of the characteristic error field of this region can be the sum of the product of the characteristic error value of each wall coordinate in this region and the corresponding local wall area; the absolute integral value of the characteristic error field of this region can be the sum of the absolute value of the characteristic error value of each wall coordinate in this region and the corresponding local wall area; wherein, the integral value of the characteristic error field of this region is used to characterize the net correction amount of the characteristic error field of this region in this region, and the absolute integral value of the characteristic error field of this region is used to characterize the total deviation intensity of the characteristic error field of this region in this region; (5) Range characteristics: can be the area ratio characteristics of the high error region; the area ratio characteristics of the high error region are preferably based on the definition of the absolute value of the error field; Specifically, for any given region, first calculate the maximum absolute value of the characteristic error values ​​of all wall coordinates within that region; then calculate the high error threshold based on this maximum value and a preset scaling factor; preferably, the preset scaling factor is a value greater than 0 and less than 1, for example, 0.8; when the absolute value of the characteristic error value of any wall coordinate is not less than the high error threshold, the wall coordinate is considered to belong to the high error zone; thus, the set of wall coordinates in the high error zone is determined; the area ratio of the high error zone can be determined based on the ratio of the local wall area corresponding to the wall coordinates within the high error zone to the local wall area corresponding to all wall coordinates within that region; further distinction can be made between positive error high value zones and negative error high value zones; the positive error threshold can be determined based on the maximum positive error value within that region; the negative error threshold can be determined based on the absolute value of the minimum negative error value within that region; the positive and negative error thresholds can be determined separately based on the preset scaling factors; and the area ratios are calculated based on the local wall areas represented by the reference points within the positive and negative error high value zones, respectively.

[0042] (6) Location features: The location of the error peak, the centroid of the main height error region, and the location features of multiple height error sub-regions can be extracted; (6.1) Location of error peak For any region on the outer wall of the first stage of the rocket, the coordinates of the wall with the largest absolute value of the characteristic error value are taken as the error peak position of the characteristic error field of that region; wherein, the parameter coordinates can be represented by axial coordinates and circumferential coordinates, radial coordinates and angular coordinates, planar coordinates, generatrix arc length and circumferential angle, or local surface coordinates, depending on the region. (6.2) Centroid location of the main high error region: For any region, when the high error region corresponding to the set of high error region wall coordinates is composed of multiple high error sub-regions that are not connected to each other, it is preferable to first divide the high error region into connected domains in the parameter coordinate space of the corresponding region, and merge the high error reference points that are connected to each other into the same high error sub-region; select the main high error region from the multiple high error regions, and extract the centroid location to obtain the centroid location of the main high error region; (6.3) Location characteristics of multiple high error sub-regions: The centroid position of each high error region can be calculated separately to form a set of multiple hot spot location characteristics; and the area, absolute integral value and peak intensity of each hot spot corresponding to the high error region are further retained as auxiliary descriptive information of the multiple hot spot location characteristics, which can be used for subsequent target working condition error field location constraints or hot spot migration analysis. (7) Distribution characteristics of the characteristic error field: Obtain the average distribution characteristics and peak distribution characteristics of the characteristic error field along the main parameter direction in this region, which are used to characterize the strength variation, distribution non-uniformity and local high value segment variation of the error field in different parameter directions.

[0043] Step S140: Based on multiple characteristic error fields, basic heat flow characteristics, and target basic heat flow distribution data, determine the wall thermal load of the rocket's first stage during the recovery process.

[0044] The wall thermal loads of the rocket's first stage during recovery can include one or more of the following: different wall coordinates of the rocket's first stage outer wall, the coordinate number corresponding to each wall coordinate, the area identifier of the assigned area, the heat flux density of the first wall, the heat flux density of the second wall, the heat flux peak, the range of the high-heat zone, the heat flux integral, and the heat flux distribution characteristics. The wall thermal loads of the rocket's first stage during recovery can be further used for one or more of the following: rocket thermal environment analysis, thermal protection design and evaluation, thermal load comparison analysis of different rocket body configurations or nozzle operating modes, rapid evaluation of thermal loads under multiple operating conditions, and thermal load analysis during recovery.

[0045] In specific implementation, the target operating condition parameters are processed to obtain the target operating condition feature vector; the target operating condition feature vector is used to characterize the differences in the target operating condition in terms of flight state, jet action state, and rocket body configuration; preferably, the target operating condition feature vector may include one or more of the following: flight altitude, incoming flow velocity, incoming flow Mach number, incoming flow density, incoming flow pressure, incoming flow temperature, number of working nozzles, total jet outlet pressure, total jet outlet temperature, jet mass flow rate, jet velocity, and parameters characterizing the rocket body geometry and nozzle arrangement; Calculate the association weight between the target operating condition feature vector and the corresponding sample operating condition feature vectors in the sample operating condition database. Specifically, the association weight between the target operating condition feature vector and the corresponding sample operating condition feature vectors can be calculated based on the feature distance between the target operating condition feature vector and each sample operating condition feature vector. Alternatively, the association weight between the target operating condition feature vector and each sample operating condition feature vector can be determined based on the target operating condition feature vector and each sample operating condition feature vector. For sample operating conditions that do not belong to the neighborhood sample set of the target operating condition feature vector, the association weight of their corresponding sample operating condition feature vector is reset to zero. The association weight between the target operating condition feature vector and each sample operating condition feature vector can also be determined using a kernel function method; for example, a Gaussian kernel function can be used to determine the association weight. For any region on the outer wall of the first stage of a rocket, the feature error field of each sample feature vector in that region is reconstructed by using the correlation weights between the target feature vector and the feature vectors of each sample feature vector, thus obtaining the target feature error field of each sample feature vector in that region. Specifically, the reconstruction can be performed using an error field transfer method based on correlation weights, an interpolation method based on local neighborhoods, an error field prediction method based on surrogate models, or other error field reconstruction methods based on the feature space of the feature parameters. Based on the target characteristic error field, basic heat flow characteristics, and target basic heat flow distribution data of different regions, the wall thermal load of the rocket's first stage during the recovery process is determined.

[0046] In one embodiment of this application, to avoid unreasonable impacts on subsequent correlation calculations due to differences in the dimensions and numerical ranges of different target operating condition parameters, the target operating condition parameters are processed to obtain a target operating condition feature vector, which may include: The target operating condition parameters are preprocessed to obtain preprocessed target operating condition parameters. Specifically, for any target operating condition parameter, a dimensionless reference quantity method can be used for processing. The reference quantity can be determined based on statistical results from a sample operating condition database or preset engineering reference values. Alternatively, a maximum / minimum value normalization method can be used. Or, a standardized processing method can be used. The maximum value, minimum value, mean, and standard deviation can be determined from the statistical results of the corresponding sample operating condition parameters in the sample operating condition database. Based on the preprocessed target operating condition parameters, a target operating condition feature vector is generated; or, the preprocessed target operating condition parameters are weighted to generate a target operating condition feature vector. The weight coefficients corresponding to each target operating condition parameter can be determined based on the degree of influence of the operating condition parameters on the wall thermal load, engineering experience, or statistical results from the sample operating condition database.

[0047] In another embodiment of this application, the feature error field of each sample working condition feature vector in the region is reconstructed using the correlation weights between the target working condition feature vector and each sample working condition feature vector, thereby obtaining the target feature error field of the region. This may include: First, error field transfer and reconstruction based on working condition similarity under small sample conditions: When the number of sample working conditions is small, the characteristic error fields of each sample working condition can be transferred and reconstructed by weighting the correlation weight between the target working condition and the sample working conditions. The characteristic error field of the target working condition in this region can be determined by weighting the correlation weights and characteristic error fields corresponding to each sample working condition. Secondly, instead of directly weighting the point values ​​of the characteristic error field, the characteristic quantities of the characteristic error field can be predicted first, and then the characteristic error field of each sample working condition can be reconstructed based on the prediction results. Preferably, one or more of the following can be predicted: the absolute peak value, mean value, absolute integral value, area ratio of high error region, and centroid position of the main high error region.

[0048] Third, error field prediction and reconstruction based on surrogate model under large sample conditions: When the number of sample operating conditions is large, an error field surrogate model can also be established based on the mapping relationship between the feature vector of sample operating condition parameters and the error field of the corresponding region; for each region, a corresponding mapping relationship can be established; the input of the error field surrogate model is the feature vector of sample operating condition parameters, and the output is the error field of the corresponding region; among them, the feature vector of sample operating condition parameters can be obtained by preprocessing the sample operating condition parameters in the sample operating condition library, and the error field of the corresponding region can be constructed by the difference between the reference heat flow distribution data and the basic heat flow distribution data under the sample operating condition.

[0049] Preferably, the surrogate model may be one or more of the following: Gaussian process regression model, support vector regression model, artificial neural network model, deep neural network model, convolutional neural network model, radial basis function neural network model, or other machine learning surrogate models; The error field of the region corresponding to the target working condition can be predicted by the error field surrogate model based on the working condition feature vector of the target working condition; wherein, the working condition feature vector of the target working condition can be obtained by preprocessing the target working condition parameters; The target object predicted by the above proxy model is not the complete heat flow field of the target operating condition, but the characteristic error field of the reference heat flow model relative to the basic heat flow model. In this way, the complexity of predicting the thermal load of the target operating condition is reduced by using the method of separating the basic heat flow main field and the error field.

[0050] In one embodiment of this application, the wall thermal load of the rocket's first stage during recovery is determined based on the target characteristic error field, basic heat flow characteristics, and target basic heat flow distribution data for different regions, including: For any region on the outer wall of the first stage of a rocket, the basic heat flow characteristics of that region are used to correct the target feature error field of that region, thus obtaining the corrected target feature error field of that region. Based on the corrected target characteristic error fields and the target basic heat flow distribution data for this region, the wall thermal load for this region is generated; among them, the wall thermal load of the first stage of the rocket during the recovery process is... ;in, This represents the wall thermal load at the coordinates of the i-th wall in the b-th region; This represents the heat flux density of the first wall at the coordinates of the i-th wall in the b-th region; Indicates the b-th region The corrected target feature error fields corresponding to each wall coordinate; the meaning of the above superposition process is that: the first wall heat flux density, i.e. the target basic heat flux distribution data, is used to provide the dominant distribution structure of the target working condition wall heat flux, and the target feature error field is used to provide the correction information of the reference heat flux model relative to the basic heat flux model. The target working condition wall heat load is quickly reconstructed by superimposing the two. Based on the wall thermal load of each region, the wall thermal load of the first stage of the rocket during the recovery process is obtained.

[0051] In the above embodiments of this application, the correction does not directly change the primary heat flux field of the target operating condition, but rather uses the heat flux intensity level and spatial distribution characteristics characterized by the primary heat flux field of the target operating condition to adjust the amplitude or spatial distribution of the error field; the correction of each target characteristic error field in any region of the outer wall of the first stage of the rocket is obtained by using the primary heat flux characteristics of that region to correct the error fields of each target characteristic in that region, which may include: Based on the intensity characteristics in the basic heat flux characteristics of the region and the preset overall amplitude adjustment coefficient, the overall amplitude of the error field of different target characteristics in the region is adjusted; wherein, the preset overall amplitude adjustment coefficient can be determined according to the difference between the basic heat flux intensity characteristics of the target operating condition and the basic heat flux intensity characteristics of the sample operating condition; Based on the distribution characteristics of the basic heat flow characteristics in the region, spatial adjustments are made to the error fields of different target characteristics in the region; specifically, spatial adjustments are made based on the normalized distribution of the target basic heat flow data in the region, the preset minimum protection coefficient, and the preset distribution constraint parameters. Based on the upper limit of the allowable amplitude of the error field of different target features in the region, amplitude limiting processing is performed on each target feature error field. The upper limit of the allowable amplitude of each target feature error field can be a preset fixed threshold or can be automatically determined based on the sample working condition error field. Preferably, the upper limit of the allowable amplitude of the target feature error field can be determined based on the sample working condition error field, the correlation weight, and the preset safety amplification coefficient. Through the above amplitude limiting processing, the error field value exceeding the allowable range can be limited to a preset interval, thereby suppressing the local abnormal amplification caused by insufficient coverage of the sample working condition, excessive feature distance between the target working condition and the sample working condition, or extrapolation by the surrogate model, and improving the stability of the target working condition error field reconstruction result.

[0052] In another embodiment of this application, in order to reduce the possible abrupt changes in heat flux at the boundary between different wall partitions, the method may further include: The boundary of any two adjacent regions is pre-defined as a transition region. Within the transition region, the wall heat loads corresponding to different regions are smoothly weighted to obtain the optimized wall heat load of the transition region. Specifically, the optimized wall heat load of the transition region is determined based on the pre-defined boundary transition weight and the heat flow results corresponding to two adjacent partitions within the boundary neighborhood. The pre-defined boundary transition weight can be set to be greater than or equal to 0 and less than or equal to 1. Preferably, the boundary transition weight can be continuously varied according to the distance from the wall coordinates to the region boundary.

[0053] This application is applicable to the rapid evaluation of the wall heat flux of the first stage of a rocket under different flight conditions, jet working modes and rocket body configuration parameters. It can improve the efficiency of wall heat load acquisition while reducing the number of times the reference heat flux model is directly called.

[0054] This application does not directly predict the overall wall heat load of the target operating condition, but rather constructs a rapid prediction framework based on the fundamental heat flux field and the correction of the regional error field. For the sample operating condition, the corresponding wall heat flux data are obtained using both the fundamental heat flux model and the reference heat flux model. The fundamental heat flux model provides the dominant heat flux distribution of the target operating condition, while the reference heat flux model provides correction information relative to the fundamental heat flux model. The fundamental heat flux model and the reference heat flux model can be selected according to the heat flux calculation requirements. Preferably, a uniform flow model and a multi-component diffusion model, or a freezing flow model and a reactive flow model, can be used respectively.

[0055] This application maps wall heat flux results under different operating conditions and models to a unified reference point set, and further divides the outer wall of the rocket's first stage into zones to separately handle the heat flux distribution and error distribution patterns of the nose region, cylindrical wall region, and base region. Furthermore, this application constructs error fields for each wall zone of each sample operating condition, forming a zone error field expression unit containing zone identifiers, error field data, and error field characteristics. For the target operating condition, the basic heat flux model is first used to obtain the main wall heat flux field, and the corresponding zone heat flux characteristics are extracted. Then, based on the correlation between the target operating condition and the sample operating condition in the operating condition parameter feature space, the error field expression units of the corresponding zones in the sample operating condition are called, weighted, or interpolated to obtain the error fields of each zone in the target operating condition, and constrained by the corresponding zone heat flux characteristics of the target operating condition. Finally, the error fields of each zone are superimposed onto the basic heat flux main field of the target operating condition to form a rapid prediction result of the wall thermal load of the target operating condition.

[0056] The essence of this application is not to directly predict the overall wall thermal load of the target working condition, but to achieve rapid, stable and consistent prediction of the wall thermal load during the recovery of the first stage of the rocket by dividing and cooperating the basic heat flow model and the reference heat flow model, constructing a unified wall data space, parametrically expressing the wall partitions, organizing the partition error field expression units, and reconstructing the error field under the constraint of the heat flow characteristics of the target working condition.

[0057] Corresponding to the above method, this application also provides a device for determining the wall thermal load during the recovery of a rocket's first stage, such as... Figure 2 As shown, the device includes: Acquisition unit 210 is used to acquire target operating parameters of the first stage of the rocket during the recovery process and target basic heat flow distribution data of the outer wall of the first stage of the rocket. The processing unit 220 is used to process the target basic heat flow distribution data to obtain the basic heat flow characteristics; The filtering unit 230 is used to filter multiple sample operating parameters associated with the target operating parameters and the corresponding feature error fields from a pre-built sample operating condition library. The determination unit 240 is used to determine the wall thermal load of the rocket's first stage during the recovery process based on multiple characteristic error fields, basic heat flow characteristics, and target basic heat flow distribution data.

[0058] The functions of each unit in the device for determining the wall thermal load during the recovery of the first stage of a rocket provided in the above embodiments of this application can be implemented through the above-described method steps. Therefore, the specific working process and beneficial effects of each unit in the device for determining the wall thermal load during the recovery of the first stage of a rocket provided in the embodiments of this application will not be repeated here.

[0059] This application also provides an electronic device, such as... Figure 3 As shown, it includes a processor 310, a communication interface 320, a memory 330, and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other through the communication bus 340.

[0060] Memory 330 is used to store computer programs; When the processor 310 executes the program stored in the memory 330, it performs the following steps: Acquire target operating parameters of the rocket's first stage during the recovery process and target basic heat flow distribution data of the rocket's first stage outer wall surface; The target basic heat flux distribution data are processed to obtain the basic heat flux characteristics; From a pre-built sample operating condition library, multiple sample operating condition parameters associated with the target operating condition parameters and the corresponding characteristic error fields of the sample operating condition parameters are selected. Based on multiple characteristic error fields, basic heat flow characteristics, and target basic heat flow distribution data, the wall thermal load of the rocket's first stage during the recovery process is determined.

[0061] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0062] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0063] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0064] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0065] The implementation methods and beneficial effects of the various components of the electronic device in the above embodiments for solving the problem can be found in [reference needed]. Figure 1 The steps in the illustrated embodiments are used to implement the electronic device. Therefore, the specific working process and beneficial effects of the electronic device provided in this application will not be repeated here.

[0066] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform the method for determining the wall thermal load during the recovery process of the first stage of a rocket as described in any of the above embodiments.

[0067] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute the method for determining the wall thermal load during the recovery process of the first stage of a rocket as described in any of the above embodiments.

[0068] Those skilled in the art will understand that the embodiments in this application can be provided as methods, systems, or computer program products. Therefore, the embodiments in this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments in this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0069] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0070] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0071] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0072] Although preferred embodiments have been described in this application, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.

[0073] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of this application and its equivalents, then these modifications and variations are also intended to be included in the embodiments of this application.

Claims

1. A method for determining the wall thermal load during the recovery of a rocket's first stage, characterized in that, The method includes: Acquire target operating parameters of the rocket's first stage during the recovery process and target basic heat flow distribution data of the rocket's first stage outer wall surface; The target basic heat flux distribution data are processed to obtain basic heat flux characteristics; From a pre-built sample operating condition library, multiple sample operating condition parameters associated with the target operating condition parameters and the corresponding feature error fields are selected. Based on multiple characteristic error fields, the basic heat flow characteristics, and the target basic heat flow distribution data, the wall thermal load of the rocket's first stage during the recovery process is determined.

2. The method as described in claim 1, characterized in that, The method for obtaining the target basic heat flux distribution data includes: Based on the target operating condition parameters, a basic heat flow calculation model for the target operating condition is constructed; The basic heat flow calculation model was used to perform fluid dynamics simulation on the external flow field and jet coupled flow field of the first stage of the rocket under the target operating conditions, and the basic heat flow distribution data of the outer wall of the first stage of the rocket was obtained. The basic heat flux distribution data is processed to obtain the target basic heat flux distribution data.

3. The method as described in claim 2, characterized in that, The basic heat flux distribution data includes: different wall coordinates and the wall heat flux density corresponding to each wall coordinate; The basic heat flux distribution data is processed to obtain the target basic heat flux distribution data, including: For any wall coordinate in the basic heat flow distribution data, determine the region of the outer wall of the first stage of the rocket to which the wall coordinate belongs; For any given region, based on the pre-configured mapping parameters for that region, coordinate system transformation and mapping processing are performed on the basic heat flow distribution data of each wall coordinate within that region to obtain the target basic heat flow data of each wall coordinate.

4. The method as described in claim 3, characterized in that, The target basic heat flux distribution data is processed to obtain basic heat flux characteristics, including: For any region on the outer wall of the first stage of a rocket, feature analysis is performed on the target basic heat flow data of all wall coordinates within the region to obtain the basic heat flow characteristics of the region; wherein, the basic heat flow characteristics include: intensity characteristics, range characteristics, location characteristics and distribution characteristics.

5. The method as described in claim 3, characterized in that, The sample operating condition library contains different sample operating condition parameters, sample operating condition feature vectors corresponding to the sample operating condition parameters, and basic heat flow characteristics, reference heat flow characteristics, and feature error fields of each region. Based on multiple characteristic error fields, the basic heat flow characteristics, and the target basic heat flow distribution data, the wall thermal load of the rocket's first stage during recovery is determined, including: The target operating condition parameters are processed to obtain the target operating condition feature vector; Calculate the correlation weight between the target working condition feature vector and the sample working condition feature vectors corresponding to the selected multiple feature error fields; For any region on the outer wall of the first stage of the rocket, the feature error field of each sample feature vector in the region is reconstructed by using the correlation weight between the target feature vector and each sample feature vector, so as to obtain the target feature error field of the region. Based on the target characteristic error field, basic heat flow characteristics, and target basic heat flow distribution data of different regions, the wall thermal load of the rocket's first stage during the recovery process is determined.

6. The method as described in claim 5, characterized in that, Based on the target characteristic error field, basic heat flow characteristics, and target basic heat flow distribution data for different regions, the wall thermal loads of the rocket's first stage during recovery are determined, including: For any region on the outer wall of the first stage of a rocket, the basic heat flow characteristics of the region are used to correct the target feature error field of the region, so as to obtain the corrected target feature error field of the region. Based on the corrected target feature error fields of the region and the target basic heat flow distribution data of the region, the wall heat load of the region is generated. Based on the wall thermal load of each region, the wall thermal load of the first stage of the rocket during the recovery process is obtained.

7. The method as described in claim 6, characterized in that, Based on the corrected target characteristic error fields of the region and the target basic heat flux distribution data of the region, the wall thermal load of the region is generated, including: The wall thermal load of the region is obtained by superimposing the corrected target feature error fields of the region with the target basic heat flow distribution data of the region.

8. A device for determining the wall thermal load during the recovery of a rocket's first stage, characterized in that, The device includes: The acquisition unit is used to acquire the target operating parameters of the first stage of the rocket during the recovery process and the target basic heat flow distribution data of the outer wall of the first stage of the rocket. The processing unit is used to process the target basic heat flow distribution data to obtain basic heat flow characteristics; The filtering unit is used to filter multiple sample operating parameters associated with the target operating parameter and the corresponding feature error fields from a pre-built sample operating condition library. The determination unit is used to determine the wall thermal load of the rocket's first stage during the recovery process based on multiple characteristic error fields, the basic heat flow characteristics, and the target basic heat flow distribution data.

9. An electronic device, characterized in that, The electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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