CN117313248A - Vehicle body structure optimization method and system considering vehicle body performance and collision injury - Google Patents
Vehicle body structure optimization method and system considering vehicle body performance and collision injury Download PDFInfo
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Abstract
The invention belongs to the technical field of vehicle body structure design, and provides a vehicle body structure optimization method and system considering vehicle body performance and collision injury, wherein the method comprises the following steps: generating a plurality of groups of solutions in a solution set space, wherein each group of solutions consists of the thickness of each plate; for each group of solutions, after genetic operation, predicting to obtain a driver injury value, vehicle body quality and a first-order mode of the white vehicle body by adopting a neural network; if the quality of the predicted vehicle body is not increased and the first-order mode of the white vehicle body is not reduced before a certain group of solutions are compared with genetic operation, the group of solutions are reserved; updating an optimal solution by adopting a reserved solution based on the injury value of the driver; judging whether iteration is terminated, if not, returning to genetic operation; if yes, decoding the optimal solution into the thickness of each plate. The method not only reduces the injury value of the driver, but also improves the NVH performance of the vehicle body, and avoids the defect that single-objective optimization is not suitable for each other.
Description
Technical Field
The invention belongs to the technical field of vehicle body structure design, and particularly relates to a vehicle body structure optimization method and system considering vehicle body performance and collision injury.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The early vehicle research and development are mostly carried out by using a real vehicle collision test, and the characteristics of experience and test are greatly relied on, so that the accuracy is low, the research and development period is long, and the research and development cost is extremely high. In order to shorten the research and development period and reduce the cost, a collision analysis method, a multi-rigid-body dynamics method and a finite element method are sequentially provided. The finite element method has the advantages of high accuracy, easy model modification, stable result and the like, and is widely applied to relevant researches on the collision safety of automobiles. Along with continuous research and exploration of students at home and abroad, certain research results have been obtained in the aspect of automobile collision safety.
Compared with the frontal collision, the 25% frontal offset collision is more prone to injuring personnel due to different energy absorption modes, and is one of the working conditions which are most difficult to overcome for the vehicle collision safety. With the continuous and deep research, aiming at the problem, a certain result is achieved by a plurality of students. Through analyzing the transmission path in the collision, the structure, the material thickness and the like of the vehicle are optimized, and a good optimizing effect is obtained, so that the safety of the vehicle in the collision is improved to a certain extent.
In the related vehicle body structure optimization research, when the optimization is carried out, only the collision safety of the vehicle is considered, other performances are ignored, and the other performances of the vehicle are reduced in the process of improving the collision safety.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides a vehicle body structure optimization method and system considering vehicle body performance and collision injury, and aims to explore a method for optimizing vehicle body design by combining a neural network and a genetic algorithm by taking the NVH performance of a vehicle body as constraint conditions and taking the first-order mode of the vehicle body and the vehicle body quality as the optimization target and taking the minimum injury value of a driver as the constraint condition, so that the injury value of the driver is reduced, the NVH performance of the vehicle body is improved, and the defect that single-target optimization is not suitable for each other is overcome.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a first aspect of the present invention provides a vehicle body structure optimization method that takes into account vehicle body performance and collision damage.
A method of optimizing a vehicle body structure in consideration of vehicle body performance and collision injury, comprising:
generating a plurality of groups of solutions in the solution set space, predicting to obtain a driver injury value, a vehicle body quality and a first-order mode of the white vehicle body for each group of solutions by adopting a neural network, and selecting a group of solutions as an optimal solution based on the driver injury value; wherein each set of solutions consists of the thickness of each plate;
for each group of solutions, after genetic operation, predicting to obtain a driver injury value, vehicle body quality and a first-order mode of the white vehicle body by adopting a neural network; if the quality of the predicted vehicle body is not increased and the first-order mode of the white vehicle body is not reduced before a certain group of solutions are compared with genetic operation, the group of solutions are reserved; updating an optimal solution by adopting a reserved solution based on the injury value of the driver; judging whether iteration is terminated, if not, returning to genetic operation; if yes, decoding the optimal solution into the thickness of each plate.
Further, the driver injury value is an indicator of head injury of the driver in the offset collision.
Further, the body-in-white first-order modality includes: a first order torsional mode of the body in white and a first order bending mode of the body in white.
Further, the plate member includes: front rail outer plate, front rail inner plate, upper rail, rail connecting beam outer plate, rail connecting beam inner plate, front coaming, side wall, front column upper inner plate, front column lower inner plate, ceiling, center column inner plate and/or back tail plate.
Further, the neural network employs a recurrent neural network having local memory cells and local feedback connections.
Further, based on the noise, vibration and acoustic vibration roughness of the vehicle body, each solution is determined to be composed of the thickness of each plate through the whole vehicle collision simulation analysis and the plate acoustic sensitivity analysis.
Further, the genetic manipulation includes crossover and mutation.
A second aspect of the present invention provides a vehicle body structure optimization system that takes into account vehicle body performance and collision damage.
A vehicle body structure optimization system that accounts for vehicle body performance and collision damage, comprising:
an initialization module configured to: generating a plurality of groups of solutions in the solution set space, predicting to obtain a driver injury value, a vehicle body quality and a first-order mode of the white vehicle body for each group of solutions by adopting a neural network, and selecting a group of solutions as an optimal solution based on the driver injury value; wherein each set of solutions consists of the thickness of each plate;
an optimizing module configured to: for each group of solutions, after genetic operation, predicting to obtain a driver injury value, vehicle body quality and a first-order mode of the white vehicle body by adopting a neural network; if the quality of the predicted vehicle body is not increased and the first-order mode of the white vehicle body is not reduced before a certain group of solutions are compared with genetic operation, the group of solutions are reserved; updating an optimal solution by adopting a reserved solution based on the injury value of the driver; judging whether iteration is terminated, if not, returning to genetic operation; if yes, decoding the optimal solution into the thickness of each plate.
A third aspect of the present invention provides a computer-readable storage medium.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps in the deep neural network training computational performance prediction method of the first aspect described above.
A fourth aspect of the invention provides a computer device.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the deep neural network training computational performance prediction method of the first aspect described above when the program is executed.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, the NVH performance of the vehicle body is considered, the first-order mode and the vehicle body quality of the vehicle body are taken as constraint conditions, the minimum injury value of the driver is taken as an optimization target, and the method for optimizing the vehicle body design by combining the neural network and the genetic algorithm is explored, so that the injury value of the driver is reduced, the NVH performance of the vehicle body is improved, the defect that single-target optimization is considered is avoided, and a borrowable method is provided for developing multidisciplinary optimization design research of the vehicle body structure.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is a schematic illustration of a 25% offset collision model according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a BIP body model in accordance with one embodiment of the present invention;
FIG. 3 is a schematic view of a vehicle-dummy collision model according to an embodiment of the invention;
FIG. 4 is a schematic illustration of a 25% offset crash front deformation and overall vehicle attitude of a vehicle in accordance with an embodiment of the invention;
FIG. 5 is a graph of 25% offset crash energy change and mass increase for an embodiment of the invention;
FIG. 6 is a graph of three-way acceleration measurements of the head of an occupant, according to an embodiment of the invention;
FIG. 7 is a graph of resultant acceleration of the head of an occupant in accordance with an embodiment of the present invention;
FIG. 8 is a first order torsional mode shape diagram of a vehicle body structure according to an embodiment of the present invention;
FIG. 9 is a first order bending mode shape diagram of a vehicle body structure according to an embodiment of the invention;
FIG. 10 (a) is a schematic diagram of the first order torsional mode sensitivity according to the first embodiment of the present invention;
FIG. 10 (b) is a schematic diagram of the first order bending mode sensitivity according to the first embodiment of the present invention;
fig. 10 (c) is a schematic view of the body mass sensitivity shown in the first embodiment of the present invention;
FIG. 11 is a topology diagram of an Elman neural network according to an embodiment of the present invention;
fig. 12 is a flowchart of genetic algorithm optimization according to an embodiment of the present invention.
Detailed Description
The invention will be further described with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
It is noted that the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of the present invention. It should be noted that each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the logical functions specified in the various embodiments. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted 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 special purpose hardware-based systems which perform the specified functions or operations, or combinations of special purpose hardware and computer instructions.
Example 1
The embodiment provides a vehicle body structure optimization method considering vehicle body performance and collision damage.
Taking a certain type of minicar as an example on the basis of summarizing the study on the collision of vehicles, the vehicle body structure optimization method considering the performance of the vehicle body in terms of NVH (noise, vibration and harshness) is taken into consideration when the optimization of the collision safety is carried out, the first-order mode of the vehicle body and the mass of the vehicle body are taken as constraint conditions, the head injury value of a driver is minimum as an optimization target, and a multidisciplinary optimization design method comprehensively considering the collision safety and the NVH performance of the vehicle by using a neural network algorithm is explored.
1. And establishing a finite element model.
(1.1) a complete vehicle collision finite element model.
Road traffic accidents mainly include front collisions, wherein the front collisions with the overlapping rate smaller than 25% have high incidence rate, and the energy absorption mode and the force transmission mode are different from other front collisions, so that drivers and passengers are more likely to be fatally injured. In 2016, a 25% small offset collision is newly added to the evaluation procedure of the C-IASI, but the evaluation result in the last 3 years shows that only less than 1/3 of the vehicle types can obtain excellent ratings, so that optimization of a part of the vehicle types in the market is needed, the collision safety of the vehicle is tested by 25% offset collision, the 25% offset collision working condition with the speed of 64km/h in the C-IASI test is selected, and the damage condition of members is analyzed.
As shown in fig. 1, the total mass of the model was 962.914kg, the number of nodes 1950979, and the number of units 1931233, wherein the triangular units 61366 occupy about 4.12%. According to the precision requirement of collision simulation, the mesh size of the target unit is 8mm, and the mesh range of the whole vehicle is 4-10mm. The vehicle model is preprocessed by Hypermesh software and solved by LS-DYNA solver.
(1.2) finite element model of vehicle body.
Two models are commonly available for modal analysis of body-in-white, BIW refers to a bolted impact energy absorbing structure through welded body parts, excluding glass, vehicle doors, engine hood panels, sunroofs, trunk lids, fenders, and the like. As shown in fig. 2, the BIP is formed by adding front and rear windshields and triangular windows on the basis of the BIP, and the overall torsion and overall bending modes can be generally identified for the BIP.
Compared with a collision model, the vehicle NVH analysis model is simpler, and only the structures such as the white vehicle body plate, the auxiliary frame, the vehicle body welding spots, the welding seams, the adhesive and the like are reserved. When NVH performance analysis is carried out, the deformation of the automobile body part belongs to linear elastic deformation, so that the type of material cards of the structures such as the automobile body plate, the closing piece and the welding spots is MAT1, and the material parameter attribute is the density, the elastic modulus and the Poisson ratio of the corresponding material. In order to improve the precision, the CNRB rigid unit of the LS-DYNA collision model is modified into an RBE2 unit of OptiStrect; furthermore, spot welds in the optigstruct model were modeled using ACM (Shell Gap) elements with the same elastic properties as the welded components. The model has 550602 shell units, 2976 ACM welding spot units and 1496 RBE2 units.
2. And (5) performing whole car collision simulation analysis.
(2.1) a 25% offset collision simulation model of the whole vehicle.
From the birth of the first crash dummy, the vehicle crash test dummy is developed into different types from a single dummy model at the beginning, and the test data of different groups during the crash can be obtained through different dummy models. Among them, the most widely used are Hybrid iii dummies and THOR dummies, which are subsequent models of Hybrid iii dummies, and have been further improved in a great number on the basis of compensating for the disadvantages of Hybrid iii dummies, and sensors at the parts such as the face, neck, chest, etc. have been newly added, so that the THOR dummies can truly and accurately reflect injuries suffered by passengers when actual collision occurs, compared with Hybrid iii dummies.
The THOR dummy model of the 50 th percentile is used for simulating the injury suffered by the driver in the case of real collision in the collision process. As shown in fig. 3, a vehicle dummy model is shown. The THOR-50M dummy model has the same material properties, physical structure and mechanical response characteristics as its physical dummy. The restraint system includes a two-dimensional belt model, a steering wheel mounted airbag, and a side curtain.
For frontal 25% overlap rigid barrier collision simulation, the vehicle model collides against the rigid barrier at a speed of 64km/h. The solution was performed using an LS-DYNA solver with a solution time set to 150ms.
(2.2) analysis of collision results.
In traffic accidents, front collision accidents often occur at a high rate, and injuries are large, and because front collision often accompanies high running speed and high kinetic energy, on one hand, parts such as an engine and the like can be impacted strongly and then squeeze the front coaming, so that living space of front passengers is compressed. On the other hand, under the action of huge impact inertia force, the head and chest of the passenger are seriously injured, and death is often caused.
In the 25% offset collision test, the overlapping width of the front part of the vehicle head and the obstacle avoidance is 25%, and in the case of collision, the impact of the collision position is large because the contact surface of the vehicle head and the obstacle avoidance is small, so that the collision safety of the vehicle body under the limit condition is a great test. In contrast to the 40% offset deformable obstacle avoidance used for offset small overlap collisions in the C-NCAP test, the 25% offset collisions according to the C-IASI of the present embodiment, using a rigid obstacle avoidance for offset collision analysis, is more prone to simulating collisions of vehicles with walls, utility poles, etc.
According to the C-IASI, the collision is set to be a non-deformable barrier, and the initial speed of the whole car is 64km/h. The calculation time is set to be 150ms through the control card, the solving file is derived after the control cards such as energy control, time step, generation file and the like are set, and the solving file is submitted to the LsDyna solver for solving.
Fig. 4 shows the deformation and posture of the front portion of the vehicle during a 150ms collision. In the whole collision process, the left front part is greatly deformed, the front part of the left side A column is locally deformed, and after the collision for about 100ms, the stress is concentrated on the left side, so that the tail part of the vehicle body is greatly transversely moved in the collision. The effectiveness of the model in 25% offset collision was also analyzed in terms of energy and mass changes during the collision.
As shown in fig. 5, from the energy curve, the total energy remains substantially unchanged during the whole collision, the total energy is mainly in the form of kinetic energy at the beginning of the collision, the kinetic energy reaches a minimum value at about 70ms, the internal energy reaches a maximum value, and the mass increase during the collision is about 12.5kg, which is about 0.82% of the total mass. The energy and mass change of the 25% offset collision model are in a normal range, and the energy change of the calculation model is reasonable, and the hourglass energy ratio and the mass increase are in a normal range, so that the method can be used for subsequent research and analysis.
Head injury is one of the main injury modes in traffic accidents, and the head injury with different degrees can cause serious injury to people and death of passengers. The embodiment mainly researches the damage condition of the head of the passenger in the traffic accident. The injuries suffered by passengers in traffic accidents can be mainly divided into two cases, one is that the head directly collides with the vehicle interior, and the other is that brain tissues are injured due to the fact that the head is subjected to the action of inertia force.
HIC 36 Is the most commonly used index for evaluating human head injury in the current test, and the calculation is shown as follows:
in the formula (1), t 1 And t 2 And a (t) is the combined acceleration of the gravity center position point of the head of the human body for the starting time and the ending time of the selected time period in the whole simulation time. T in the current test 1 And t 2 The time interval of (2) is generally 36ms, and the main reason is that the HIC value is maximum in the time interval of 36ms, so that the time interval is more suitable for the injury of passengers in real accidents. Table 1 shows the human head injury index HIC 36 Corresponding AIS class relationships.
TABLE 1 human head injury index HIC 36 Corresponding AIS grade
| AIS grade | HIC 36 | Injury condition | Degree of injury |
| 1 | 130-519 | Headache and dizziness | Mild injury |
| 2 | 520-899 | Temporary loss of consciousness; linear fracture | Moderate injury |
| 3 | 900-1254 | 1-6h of loss of consciousness; depressed fracture | Severe injury |
| 4 | 1255-1574 | Loss of consciousness for 6-24 hours; open fracture | Severe injury |
| 5 | 1574-1859 | Loss of consciousness for a long period of time; cerebral hematoma | Critical damage, life-threatening |
| 6 | >1860 | Death or inability to rescue | Killing by death |
The measurement results of the X, Y, Z three-direction accelerometer of the dummy head measuring point are derived, and the three-direction acceleration changes of the head during collision are shown in fig. 6. The X-direction acceleration is the front-back movement of the head, the X-direction acceleration curve gradually decays after reaching the peak value within 70ms-80ms, the Y-direction acceleration is expressed as the left-right movement of the head, and the curve is basically within +/-30 m/s 2 Inner wave, but the vehicle body generates a large transverse movement in collision, and reaches the peak value of the Y-direction acceleration of 69.3m/s at the end time of about 148ms 2 . The Z-direction acceleration is expressed as the pitching motion of the head, and the acceleration curve reaches the inverse peak value of-62.42 m/s at the 61.5ms 2 The forward peak value 64.56m/s is reached at 85.1ms due to the reaction force 2 And then gradually decays.
And synthesizing the three-way acceleration, and taking root of the square sum of the three-way acceleration curves to obtain a synthesized acceleration curve, wherein the synthesized acceleration curve is shown in fig. 7.
Calculating a synthetic acceleration curve according to a formula (1) to obtain HIC of a driver in the collision process 36 Value, HIC, within 59.30ms-95.30ms 36 The value reaches a peak value 845.96, and the HIC is indicated according to the human head injury index 36 The corresponding AIS grade relationship table can obtain that the corresponding human head injury grade is moderate injury, and the specific injury condition is described as temporary consciousness loss and linear fracture.
3. And (5) simulating and analyzing NVH performance of the vehicle body.
(3.1) Modal analysis.
When NVH performance analysis is carried out on a vehicle body, first-order integral modes of the white vehicle body are considered, and if the frequency value of the first-order modes is low, the first-order modes are easy to be excited by low frequency excitation such as an engine and the like to cause vehicle body vibration to generate noise, so that riding comfort of the vehicle is affected. The first order modal frequency reduction of the body-in-white should be avoided during the modal planning phase. And submitting the modal solving file to Optistruct calculation. The partial mode values and modes are shown in Table 2, and the first-order mode modes are shown in FIGS. 8 and 9. The body-in-white mass is 306.86kg.
TABLE 2 front eighth order nonzero modal frequencies and modes for body structure
| Natural frequency of | Natural frequency/Hz | Vibration type |
| First order of | 28.99 | Transverse mode of water tank support |
| Second order | 29.63 | Longitudinal mode of water tank bracket back tail plate |
| Third order of | 30.87 | Longitudinal mode of water tank bracket back tail plate |
| Fourth order of | 37.24 | Transverse mode of water tank support |
| Fifth order of | 39.75 | First order torsional mode |
| Sixth order of | 43.34 | First order bending mode |
| Seventh order of | 45.84 | Local mode of water tank support |
| Eighth order of | 48.76 | Torsional mode |
(3.2) plate acoustic sensitivity analysis.
One of the important reasons for noise generation in a vehicle is the vibration mode of a vehicle body panel, in the mode shape, a point with zero displacement or small displacement is a mode node, and the vehicle mass is distributed near the mode node as much as possible through the optimization of the structure, so that the vibration of the vehicle body can be greatly improved. The thickness of the body panel is changed without changing the design of the body profile, which not only affects the NVH performance of the vehicle, but also has a great influence on the collision safety of the vehicle. The body panel thickness is used as an important body structural parameter as a design variable in order to obtain an optimal set of panel thickness combinations that optimize crash safety while taking into account body NVH performance.
And determining constraint conditions of the first-order torsion mode and the first-order bending mode of the vehicle body for optimizing the noise in the vehicle through mode analysis. The lightweight design of the automobile can obviously improve the fuel economy of the automobile while reducing the cost, so the NVH performance of the automobile cannot be improved by reducing the lightweight performance in the optimization process, and the quality of the automobile body is added as an optimized constraint condition. And selecting and numbering the plate parts of the vehicle body, and analyzing the sensitivity of the plate parts to the thickness of the plate parts according to the bending mode and the mass of the vehicle body.
TABLE 3 sensitivity analysis variables
Input variables are defined according to table 3, defining body-in-white first order torsional mode, first order bending mode, and body mass as output responses. In the case where all factors are two-level, the Plackett Burman sampling method can calculate the primary effect of the factor with a minimum number of trials. In the sensitivity analysis, the interaction effect among the design variables is ignored, the influence of the variation of the design variables between the upper limit and the lower limit on each response is examined, and the sampling method adopts Plackett Burman.
In HyperGraph post-processing software, the sensitivity magnitude and the positive and negative of each design variable to response were checked by checking Pareto chart, and the results are shown in fig. 10 (a), 10 (b) and 10 (c).
The vertical scale in fig. 10 (a), 10 (b) and 10 (c) each show sensitivity, and an increase in the thickness of the body panel increases the body mode, so the mode sensitivity is substantially positive. The thickness of the plate is proportional to the mass of the plate, so the mass of the vehicle body is all positive in sensitivity. When the design variables are selected, the first-order torsion mode, the first-order bending mode and the first two plates with higher mass sensitivity of the vehicle body are comprehensively considered, wherein the side wall has larger influence on the mode and the mass sensitivity.
4. And predicting the injury value of the passenger.
(4.1) test design.
And finally determining the target, constraint conditions and design variables of the passenger injury prediction through the simulation analysis of the 25% offset collision of the whole vehicle and the NVH performance analysis of the vehicle body. The objective function being HIC biasing the driver's head in a collision 36 Injury value. The constraint conditions are respectively: a first-order torsional mode of the white body; a first order bending mode; body mass (body in white, closure only). The variables of the following test designs, which are 12 plates such as the front side member outer plate, the front side member inner plate, the upper side member, etc., are shown in table 4.
Table 4, test design variables.
In the experimental design, various sampling methods are available, and methods such as full factor design, latin hypercube, tian Koufa, hammerley sampling and the like are commonly used. The advantage of Hammersley sampling is that fewer samples can be used to provide a reliable estimate of the output statistics. Meanwhile, the method can obtain good uniform distribution on the k-dimensional hypercube, and the identification process of Hammerley sampling is relatively efficient. It only needs to sample on the input and output signals, without having to recognize the internal state of the system. The Hammerley sampling method is selected for design of experiments (DOE) to obtain subsequent training and testing sets.
(4.2) Elman neural network.
A neural network is a network formed by interconnecting a plurality of neurons. The neural network can be divided into two basic types, namely a feedforward neural network (Feedforward Neural Networks) and a feedback neural network (Feedback Neural Networks) according to the information flow direction in the network operation. The feed forward network relies on a hidden layer and a nonlinear transfer function to implement complex nonlinear mapping functions. The output of the feed forward network is related only to the current output and weight matrix, independent of the previous network output results. In contrast, the input of a feedback neural network comprises a delayed input or feedback of output data, so it is a feedback kinetic system, and thus the feedback neural network is also called recurrent neural network or regression network.
The topology of Elman neural network (recurrent neural network with local memory unit and local feedback connection) is shown in fig. 11, and an accepting layer is added in the hidden layer of the feedforward network to serve as an operator of one-step delay so as to achieve the purpose of memorization. Elman's neural network is generally divided into four layers: input layer, hidden layer, receiving layer and output layer. As shown, the connections of the input layer, hidden layer and output layer are similar to feed-forward networks, the units of the input layer play a role in signal transmission, and the units of the output layer play a role in weighting. The hidden layer elements have linear and nonlinear functions, and the receiving layer is used to memorize the output of the hidden layer element immediately before and return to the network as input. The self-connection mode of the Elman neural network makes the Elman neural network sensitive to past data, and meanwhile, the capacity of the network for processing dynamic information is enhanced by adding an internal feedback network, so that the Elman neural network can approximate any nonlinear mapping with any precision.
The structure of Elman neural network is shown in fig. 11, and its nonlinear spatial expression can be expressed as:
y(k)＝g(w 3 x(k)) (2)
x(k)＝f(w 1 x c (k)+w 2 (u(k-1))) (3)
x c (k)＝x(k-1) (4)
in the formula (2), y is an output vector of m dimensions; x is an n-dimensional intermediate layer node element vector; u is the r-dimensional input vector; x is x c The layer vectors are n-dimensional bearing layers; w (w) 3 Weights from the middle layer to the output layer; w (w) 2 The weight value from the input layer to the middle layer is obtained; w (w) 1 The weight from the bearing layer to the middle layer is used; g is the transfer function of the output layer neurons, which is a linear combination of the intermediate layer outputs; f is the transfer function of the middle layer neurons.
5. Elman prediction analysis.
Neural network vs. driver head HIC in collision 36 And predicting the value, the first-order mode of the white body and the body quality. Table 5 shows the driver's head HIC 36 And comparing the value prediction result with the simulation result, and in order to compare the prediction accuracy with other neural network algorithms, predicting the head injury value of the driver by using a radial basis neural network (radial neural network), a Generalized Regression Neural Network (GRNN) and a BP neural network respectively, and evaluating the prediction error by using a mean square error, a root mean square error, a mean absolute error and a mean absolute percentage error, as shown in table 5.
Table 5, algorithm predictive head injury value error contrast
As can be seen from the data in table 5, the predictive value of the Eleman neural network is highly consistent with the actual value, and the predictive error is smaller than that of other neural networks. The matching degree of the predicted value and the actual value is Eleman, GRNN, REF, BP from high to low respectively.
Mean Square ErrorMSE (MSE) represents the sum of squares of the distances between each predicted value and the actual value, with smaller values representing smaller errors and higher prediction accuracy.
Root Mean Squared Error (RMSE) is the root number of the MSE to measure the deviation of the observed value from the true value.
Mean Absolute Error (MAE) average error is compared with the average absolute error, the average absolute error is absolute-valued due to the dispersion, the positive and negative offset condition can not occur, and the actual condition of the predicted value error can be better reflected.
The MAPE (mean absolute percentage error Mean Absolute Percentage Error) is more intuitive in terms of accuracy of the predictions, somewhat analogous to the concept of amplification. The difference between the predicted value and the actual value is used to see how large the duty cycle is compared with the actual value.
The Elman neural network algorithm predicts the first order mode of the body in white, and the prediction error of the body quality is shown in table 6.
Table 6, elman neural network algorithm prediction error
| Prediction item | MSE | RMSE | MAE | MAPE |
| First-order torsional mode/Hz of white car body | 1.883×10 -2 | 1.372×10 -1 | 1.183×10 -1 | 2.985×10 -1 |
| First-order bending mode/Hz of white body | 2.845×10 -1 | 5.334×10 -1 | 3.941×10 -1 | 9.306×10 -1 |
| Vehicle body mass/kg | 2.424×10 -5 | 4.923×10 -3 | 3.603×10 -3 | 1.167 |
As can be seen from tables 5 and 6, the Elman neural network has the smallest prediction error for the head injury value of the driver, and is far superior to the other three neural network algorithms in four indexes of mean square error, root mean square error, mean absolute error and mean absolute percentage error. The Elman neural network algorithm has small prediction error when predicting the quality of the body and the first-order mode of the white body, and meets the prediction requirement. In consideration of machining precision, the step size of each input variable in the solution space is set to 0.1, and 244,140,625 possible solutions are taken in total. Predicting output corresponding to a feasible solution by adopting an Elman neural network: driver injury value, body mass, first order modality. The method is characterized in that the head injury value of a driver is taken as a target, the mass of a vehicle body is not increased, the first-order mode of a white vehicle body is not reduced to be a constraint condition, an optimal solution is found in a feasible solution set, and a mathematical expression is as follows:
considering that the solution set is too huge, the main means of solving this problem by the numerical method is iterative operation. The general iteration method is easy to trap into a local extremely small trap to cause a dead loop phenomenon, so that iteration cannot be performed. The genetic algorithm is a global optimization algorithm, which well overcomes this drawback. Compared with the traditional optimization method, the genetic algorithm takes biology as a prototype, has good convergence, short calculation time and high robustness. And optimizing in the solution set space by using a genetic algorithm. A block diagram of the algorithm flow is shown in fig. 12. Firstly, generating a plurality of groups of solutions (the thickness of each plate) in a solution collection space (namely the upper limit and the lower limit shown in the table 4), obtaining a predicted value corresponding to each group of solutions through the trained Elman neural network, and reserving the solutions until the number of the groups meets the requirement. And (3) calculating the fitness of the individuals in the population, wherein the difference of the head injury value of the driver corresponding to the initial solution minus the predicted injury value of each solution is taken as the fitness, and the larger the difference is, the better the fitness is, and the better the individuals are. And carrying out genetic, cross, mutation and other operations on the individual, then predicting by the Elman neural network, and repeating the optimizing process. And if the initial solution is the global optimal solution which is started, replacing the optimal solution until the iteration is terminated.
Optimizing the solution set space to obtain an optimal solution meeting constraint conditions of [1.7,2,1,1.3,1.5,0.7,0.8,1.3,0.6,0.8,1.2,0.8 ]]To verify the final predictive solution of the Elman neural network algorithm, the thickness of the plate with the optimal solution is givenAnd submitting the attribute of the finite element model to an Optistruct and LS-DYNA solver for simulation calculation, and obtaining an optimization result as shown in a table 7. HIC of driver's head 36 The value is reduced by 173.43, the quality of the vehicle body is reduced, the vehicle body is basically maintained at the original level, and the first-order mode is optimized to a certain extent compared with the original data.
TABLE 7 comparison of constraint responses before and after optimization
| Response to | Before optimization | Predictive value | Actual value | Prediction error% |
| Driver's head HIC 36 Value of | 845.96 | 627.1895 | 672.53 | 6.74 |
| Vehicle body mass/kg | 306.86 | 304.921 | 306.62 | 0.55 |
| First-order torsional mode/Hz of white car body | 39.749 | 40.209 | 39.972 | -0.59 |
| First-order bending mode/Hz of white body | 43.338 | 44.504 | 44.800 | 0.66 |
Based on Hypermesh software, a vehicle body simulation model and a whole vehicle collision model are established, simulation analysis of NVH and front offset collision is carried out, and an optimization design method for comprehensively considering NVH performance and front offset collision performance of a vehicle by using an Elman neural network is provided. The results prove that: the front offset collision performance of the whole vehicle is obviously improved while the NVH performance of the vehicle body is ensured, the defect that single-target optimization is not realized is avoided, and the improvement of safety while the riding comfort is improved in the design of the vehicle body is proved to be realized. The prediction error of each response predicted by the Elman neural network algorithm is within 7%, meets the prediction precision requirement, provides a reference for the application of the advanced intelligent algorithm in the prediction of the NVH performance and the collision performance of the vehicle body, and can be used in actual engineering by engineering technicians.
The excellent vehicle body structure design is always the main subject of passive safety research, and the reliable vehicle body structure design can improve the lower limit of the collision safety of the vehicle, thereby laying a good foundation for the arrangement of a subsequent safety system. The front collision is the most common accident form of traffic accidents, wherein the front collision with the overlapping rate of less than 25 percent has high incidence rate, and the energy absorption mode and the force transmission mode are different from other forms of collision, so that drivers and passengers are more easily killed and injured. Therefore, it is important to find an optimization scheme which is low in cost, light in weight and capable of reducing the requirements of driver injury and the like. In order to comprehensively consider various performances of a vehicle body and improve collision safety of the vehicle at the vehicle body design stage, in the embodiment, a certain type of mini-vehicle is taken as an example, NVH performance of the vehicle body is considered, when the collision safety is optimized, the thickness of a vehicle body plate determined by collision analysis and sensitivity analysis is taken as an input variable, and an Elman neural network is used for predicting first-order mode of the vehicle body, vehicle body quality and head injury value of a driver. And optimizing a solution set space of the selected input variable by using a genetic algorithm by using the Elman neural network which is completed through training and taking the first-order mode of a vehicle body and the mass of the vehicle body as constraint conditions and the minimum head injury value of a driver as an optimization target. On the premise of meeting constraint performance, compared with the prior art, the head injury value of a driver is reduced by about 173.43, the NVH performance of the vehicle body is improved, and a borrowable method is provided for developing multidisciplinary optimization design research of the vehicle body structure.
Example two
The present embodiment provides a vehicle body structure optimization system that takes into account vehicle body performance and collision damage.
A vehicle body structure optimization system that accounts for vehicle body performance and collision damage, as shown in fig. 7, comprising:
an initialization module configured to: generating a plurality of groups of solutions in the solution set space, predicting to obtain a driver injury value, a vehicle body quality and a first-order mode of the white vehicle body for each group of solutions by adopting a neural network, and selecting a group of solutions as an optimal solution based on the driver injury value; wherein each set of solutions consists of the thickness of each plate;
an optimizing module configured to: for each group of solutions, after genetic operation, predicting to obtain a driver injury value, vehicle body quality and a first-order mode of the white vehicle body by adopting a neural network; if the quality of the predicted vehicle body is not increased and the first-order mode of the white vehicle body is not reduced before a certain group of solutions are compared with genetic operation, the group of solutions are reserved; updating an optimal solution by adopting a reserved solution based on the injury value of the driver; judging whether iteration is terminated, if not, returning to genetic operation; if yes, decoding the optimal solution into the thickness of each plate.
It should be noted that the above modules are the same as examples and application scenarios implemented by the steps in the first embodiment, but are not limited to the disclosure of the first embodiment. It should be noted that the modules described above may be implemented as part of a system in a computer system, such as a set of computer-executable instructions.
Example III
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps in the deep neural network training calculation performance prediction method as described in the above embodiment.
Example IV
The present embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps in the deep neural network training calculation performance prediction method according to the above embodiment.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method of optimizing a vehicle body structure in consideration of vehicle body performance and collision damage, comprising:
generating a plurality of groups of solutions in the solution set space, predicting to obtain a driver injury value, a vehicle body quality and a first-order mode of the white vehicle body for each group of solutions by adopting a neural network, and selecting a group of solutions as an optimal solution based on the driver injury value; wherein each set of solutions consists of the thickness of each plate;
for each group of solutions, after genetic operation, predicting to obtain a driver injury value, vehicle body quality and a first-order mode of the white vehicle body by adopting a neural network; if the quality of the predicted vehicle body is not increased and the first-order mode of the white vehicle body is not reduced before a certain group of solutions are compared with genetic operation, the group of solutions are reserved; updating an optimal solution by adopting a reserved solution based on the injury value of the driver; judging whether iteration is terminated, if not, returning to genetic operation; if yes, decoding the optimal solution into the thickness of each plate.
2. The method for predicting performance of deep neural network training of claim 1, wherein the driver injury value is an indicator of head injury of the driver in the offset collision.
3. The method for predicting performance of training computation of deep neural network of claim 1, wherein said body-in-white first order modality comprises: a first order torsional mode of the body in white and a first order bending mode of the body in white.
4. The method for predicting training computational performance of a deep neural network of claim 1, wherein the plate comprises: front rail outer plate, front rail inner plate, upper rail, rail connecting beam outer plate, rail connecting beam inner plate, front coaming, side wall, front column upper inner plate, front column lower inner plate, ceiling, center column inner plate and/or back tail plate.
5. A deep neural network training computational performance prediction method according to claim 3, wherein the neural network employs a recurrent neural network with local memory cells and local feedback connections.
6. The method for predicting the training calculation performance of the deep neural network according to claim 1, wherein each solution is determined to be composed of the thickness of each plate through the whole car collision simulation analysis and the plate acoustic sensitivity analysis based on the noise, vibration and acoustic vibration roughness of the car body.
7. The deep neural network training computational performance prediction method of claim 1, wherein the genetic manipulation includes crossover and mutation.
8. A vehicle body structure optimization system that accounts for vehicle body performance and collision damage, comprising:
an initialization module configured to: generating a plurality of groups of solutions in the solution set space, predicting to obtain a driver injury value, a vehicle body quality and a first-order mode of the white vehicle body for each group of solutions by adopting a neural network, and selecting a group of solutions as an optimal solution based on the driver injury value; wherein each set of solutions consists of the thickness of each plate;
an optimizing module configured to: for each group of solutions, after genetic operation, predicting to obtain a driver injury value, vehicle body quality and a first-order mode of the white vehicle body by adopting a neural network; if the quality of the predicted vehicle body is not increased and the first-order mode of the white vehicle body is not reduced before a certain group of solutions are compared with genetic operation, the group of solutions are reserved; updating an optimal solution by adopting a reserved solution based on the injury value of the driver; judging whether iteration is terminated, if not, returning to genetic operation; if yes, decoding the optimal solution into the thickness of each plate.
9. A computer readable storage medium having stored thereon a computer program, which when executed by a processor performs the steps in the deep neural network training computational performance prediction method of any of claims 1-7.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps in the deep neural network training computational performance prediction method of any one of claims 1-7 when the program is executed by the processor.
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