Design method of high-reliability low-cost ton-level charging module structure

Abstract

The invention discloses a high-reliability and low-cost ton-level charging module structure design method, aims at the problems of low structural strength-weight ratio, high combustible cartridge case cost and lack of reliability verification means in the prior art, and innovatively provides a method for designing a high-reliability and low-cost ton-level charging module structure by adopting a nanofiber reinforced kraft paper-based gradient composite material. And in combination with multi-physics field coupling analysis and an intelligent evolutionary algorithm, a structure-material-process integrated design is realized. A reliability verification system based on digital twinning is constructed by establishing a probability model containing a plurality of random variables (covering uncertainty factors such as material elasticity modulus fluctuation coefficient + / -15% and impact load spectrum deviation + / -20%), and finally an optimization scheme with low cost, light weight and high reliability is obtained. According to the method, the charging safety and reliability can be improved, an economical and efficient technical path is provided for the lightweight design of the tonnage cartridge case, and the method has wide application prospects.

Description

Technical Field

[0001] The present invention belongs to the field of charge design, and particularly relates to a design method for a high-reliability and low-cost tonnage charge module structure. Background Art

[0002] Since the charge module needs to achieve high-density explosive loading in a limited space and withstand complex mechanical loads during transportation and launch, traditional metal structures often have a charge mass ratio of less than 60% due to redundant wall thickness, and the manufacturing cost is high. How to improve the charge efficiency through optimized design while ensuring structural reliability has become a key technical problem in ammunition lightweight design. Chinese Patent CN114508844A verified the key technical breakthrough of the collaborative optimization of structural parameters on the charge efficiency and reliability of the warhead by optimizing the composite material shell ply structure and interface parameters, while reducing the wall thickness by 20%-30%, the charge mass ratio was increased to 68.5%.

[0003] Currently, the research on charge modules mainly focuses on the development of new composite materials. However, in practical applications, in addition to the mechanical properties of the materials, the collaborative optimization of the structural parameters (wall thickness, aspect ratio, etc.) of the charge module has a significant impact on the bearing capacity and charge volume, and the existing technologies have not established a systematic reliability design method. Summary of the Invention

[0004] The present invention proposes a design method for a high-reliability and low-cost tonnage charge module structure, which improves the safety, reliability and functionality of the cartridge case by optimizing the structural size parameters.

[0005] The technical solution to realize the present invention is: a reliability-based low-cost tonnage charge module structure design, including:

[0006] Taking the core geometric parameters of the charge module structure as design variables, including the module shell length, wall thickness and outer diameter, and constructing a parametric design optimization model with minimizing the module structure weight as the optimization goal;

[0007] Taking the anti-explosion strength reliability, anti-impact stiffness reliability and buckling stability reliability of the charge module as multiple constraint conditions, and constructing a lightweight design model based on probability reliability theory, where the target reliability threshold is jointly calibrated by finite element transient dynamics simulation and dynamic impact test;

[0008] Adopting the sequential decoupling optimization method, decomposing the nested optimization into an alternating iterative process of deterministic design optimization and reliability analysis: in the deterministic optimization stage, the probability constraint is equivalently converted into a deterministic constraint condition through a dynamic boundary contraction strategy;

[0009] In the reliability analysis stage, based on the sampling - surrogate model, the key failure modes are quickly located, and the constraint boundary offset is corrected by combining the material property dispersion data until the convergence condition is met. Finally, a lightweight geometric parameter combination that meets the explosion load - bearing requirements is output.

[0010] Furthermore, the structural dimension parameters include the module housing thickness, length, and outer diameter;

[0011] The establishment of the design optimization model includes:

[0012] According to the geometric characteristics of the charge module structure and the structural dimension parameters, a mathematical representation model of the module housing volume is constructed;

[0013] Taking the minimization of the module housing volume as the optimization goal, iterative optimization of the structural dimension parameters is carried out.

[0014] Furthermore, the steps for establishing the lightweight model include:

[0015] Based on the manufacturing tolerances of the charge module structure, the material property fluctuation characteristics, and the dynamic distribution characteristics of the explosion shock load, the key random variables are determined;

[0016] According to the measured material property data and the load spectrum statistical results, the probability distribution model of the random variables is selected;

[0017] Combined with the anti - explosion load - bearing performance requirements of the charge module, a response function of the module housing strength and stiffness is constructed with the random variables as inputs;

[0018] Through the fusion of finite - element transient impact simulation and drop - hammer test data for modeling, a surrogate model of the stress and deformation response is generated, and a sample set is obtained by using the stratified sampling method in the random variable space. Based on high - fidelity simulation, the stress distribution and deformation characteristics of the module housing are calculated;

[0019] Based on Gaussian process regression, a surrogate model of the stress - deformation response is constructed to quantify the correlation deviation characteristics between random variable samples;

[0020] According to the ultimate shear strength and anti - explosion deformation threshold of the charge module housing material, combined with the stress - deformation response containing uncertain parameters, the strength failure criterion and the stiffness failure criterion are respectively defined;

[0021] Taking the simultaneous satisfaction of the strength failure criterion and the stiffness failure criterion with the target reliability threshold as the constraint condition, a lightweight design model is constructed.

[0022] Furthermore, the sequential decoupling optimization method includes:

[0023] Design optimization stage: Convert the probabilistic reliability constraint into an equivalent deterministic constraint, construct a deterministic optimization problem through a dynamic boundary contraction strategy, and solve to obtain a preliminary lightweight design scheme;

[0024] Reliability analysis stage: Based on the explosion shock load spectrum and material property dispersion data, identify the most likely failure points of the design scheme under key modes such as interlayer shear failure and buckling instability, and correct the offset of the constraint boundary through the feedback of drop hammer test data.

[0025] Iterative collaborative mechanism: Feed the gradient information of the most likely failure points and the statistical characteristics of material fluctuations back to the next round of design optimization, dynamically adjust the equivalent constraint boundary until the target reliability threshold and weight convergence condition are met, and achieve the collaborative optimization of the anti-explosion performance and lightweight goal of the charge module.

[0026] Furthermore, the steps of obtaining the design scheme include:

[0027] Based on the constraint boundary of the previous iteration, apply a dynamic boundary offset in the direction of the feasible region of the charge module structure to generate the equivalent approximate boundary of the probabilistic constraint for the current iteration, which serves as the constraint condition for this design scheme.

[0028] Construct a deterministic optimization model, whose objective function is to minimize the weight of the module shell, and the constraint conditions are the anti-shear strength boundary and buckling stability boundary after equivalent contraction. Solve through a parametric optimization algorithm to obtain a lightweight geometric parameter combination that meets the explosion shock scenario.

[0029] The dynamic boundary offset is jointly calibrated by material property dispersion data (such as the coefficient of variation of the interlayer shear strength of kraft paper ≥ 18%) and the random component of the explosion load, and the engineering rationality of the offset direction is verified through drop hammer tests.

[0030] The present invention includes the following innovative steps:

[0031] Material innovation: Use nano-carbon fiber (0.5wt%) modified kraft paper-based gradient composite material, and achieve an interlayer shear strength ≥ 450 MPa (a 300% increase compared to pure kraft paper) through a vacuum-assisted molding process.

[0032] Parameter expansion: Incorporate new parameters such as the coefficient of thermal expansion (CTE ≤ 5×10 -6 / °C), acoustic emission eigenvalue, nano-fiber distribution gradient coefficient, fiber concentration change rate, modal damping ratio, hygrothermal coupling deformation coefficient, material utilization rate, and impact energy dissipation rate into the optimization variable system.

[0033] Reliability system: Establish a failure model including the crack growth rate (da / dN ≤ 1×10 -6 mm / cycle) and the interface failure probability (P_f ≤ 5×10 -4 ).

[0034] Compared with the prior art, the present invention has the following remarkable advantages:

[0035] Revolutionary reduction in material costs: By replacing the traditional metal / composite materials with a multi-layer kraft paper composite shell and combining the modified starch glue pre-impregnation process, the material cost is reduced by more than 85% (the cost of a single layer of kraft paper is <0.5 yuan / ㎡). Moreover, the winding and forming process is simplified, the equipment investment is reduced by 60%, and a breakthrough reduction in the manufacturing cost of ton-level charge modules is achieved.

[0036] Reliability-driven precise lightweighting: Based on the collaborative optimization of ANSYS finite element simulation and Monte Carlo reliability assessment, on the premise of ensuring that the bearing capacity of the shell reaches the ton-level (safety factor 1.25), the structural weight is reduced by 29% compared with the traditional empirical design. At the same time, the internal charge space volume is increased by 9.2%, significantly enhancing the damage effect of the ammunition.

[0037] Industrial production compatibility: Through the seamless docking of parametric modeling and CNC paper winding machines (processing accuracy ±0.1mm), the automatic conversion from the optimization result to the production instruction is realized. The manufacturing cycle of a single module is shortened to 4 hours, the production efficiency is increased by 3 times, and the finished product rate is stable at more than 98%, meeting the mass production requirements of large-scale ammunition. Description of the Drawings

[0038] Figure 1 It is a flow chart for the lightweight design analysis of the cartridge structure. Detailed Implementation Modes

[0039] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0040] The technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that those of ordinary skill in the art can implement it. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by the present invention.

[0041] Next, the detailed implementation modes, as well as the technical difficulties and inventive points of the present invention, will be further introduced in conjunction with this design example.

[0042] Technical breakthrough points:

[0043] 1) Material system innovation: Develop nanofiber-reinforced kraft paper-based gradient composites, optimize anisotropic mechanical properties by regulating fiber orientation (alternate lamination of 0° / 45° / 90°), and it is confirmed by FTIR and DMA tests that the storage modulus of the material is increased to 12 GPa (about 3 GPa for pure kraft paper).

[0044] 2) Multi-physical field coupling analysis: Establish an analysis model including the following innovative elements:

[0045] Shock dynamics: Use LS-DYNA to simulate the propagation law of explosive shock waves in a cylindrical charge module, considering the strain rate hardening effect (σ = Kε^n when ε≥0.1, n = 0.35)

[0046] Thermodynamic coupling: Introduce the heat conduction equation and chemical reaction kinetics (Arrhenius activation energy Ea = 110 kJ / mol) to predict the performance degradation of the charge module in the extreme temperature range (-55°C to +85°C).

[0047] Vibration fatigue: Construct a load spectrum based on the Rainflow counting method to predict the fatigue life of the structure under 10 6 cycles (the inflection point of the S-N curve appears at 10 7 cycles)

[0048] Intelligent evolutionary algorithm: Develop a hybrid optimization framework:

[0049]

[0050]

[0051] Combined with Figure 1 , the present invention provides a method for designing a low-cost tonnage charge module structure based on high reliability. By reasonably optimizing the structural dimension parameters, while ensuring the strength constraint, the structural weight is minimized, and the reliability and performance of the cartridge are improved. Specifically, it includes:

[0052] Taking the dimension parameters of the charge module structure as design variables and the structural weight of the charge module as the optimization objective, establish an optimization model for the charge module structure design.

[0053] Taking the stiffness reliability of the charge module structure as a constraint, establish an optimization model for the low-cost tonnage charge module structure based on reliability theory.

[0054] Adopt the sequential optimization and reliability analysis method, and decouple the sequential iteration of deterministic optimization and reliability analysis.

[0055] The design optimization model and the reliability optimization model are alternately iteratively solved, and when the convergence condition is reached, the optimization design of the structural weight and geometric dimensions of the charge module is realized.

[0056] The above solution is a reliability optimization design method for the low-cost tonnage charge module structure based on probability metrics. This method first establishes an optimization design model for the low-cost tonnage charge module structure based on the force characteristics of the charge module structure, considering the uncertainty effects of structural strength and stiffness parameters and based on reliability theory. The optimization takes the size parameters (including thickness, length, and outer diameter) of the charge module structure as design variables, takes the structural weight as the optimization objective, and takes the stiffness reliability as the constraint. A sequential optimization and reliability analysis method is used to transform the nested optimization problem into a sequential iterative process of deterministic design optimization and reliability analysis. In the optimization stage, the original probability constraint is transformed into an equivalent deterministic constraint to form a deterministic optimization problem, and a new design solution is obtained by solving. In the reliability analysis stage, the reliability of the design solution is calculated to obtain the maximum possible failure points of each constraint and provide corresponding information for the equivalence of the probability constraint in the next iteration. The design optimization and reliability analysis are carried out alternately, and finally, the optimal design of the structural weight, size, and stiffness of the charge module is realized, providing an effective technical solution for the reliability optimization design of the low-cost tonnage charge module structure.

[0057] Step 1: Determine the failure mode:

[0058] The present invention takes the tonnage charge module as the analysis object and the actual working conditions of the charge module in the transportation, storage, and launch states as the analysis scenarios. Under the above working conditions, the main failure modes of the charge module include insufficient strength failure, stiffness failure, buckling instability, and brittle fracture failure, etc. Among them, the brittle fracture failure can be effectively suppressed by optimizing the design of the kraft paper laminated structure and the surface treatment process. For the cylindrical charge module with uniform wall thickness, its geometric characteristics and material toughness can avoid the occurrence of buckling instability. Therefore, strength failure and stiffness failure are the core factors affecting the reliability of the charge module. These two failure modes result from the structural resistance (such as material strength and stiffness) being lower than the action of external loads (such as impact, vibration, and static load pressure). Once they occur, they will cause irreversible overall deformation or structural fracture of the charge module, and then lead to serious consequences such as pyrotechnic device leakage, reduced explosion efficiency, and even accidental detonation. Based on this, this patent conducts reliability-based lightweight optimization design for the strength and stiffness failure modes to ensure the functional integrity of the charge module under extreme loads.

[0059] Step 2: Selection of random variables and determination of the joint probability density function:

[0060] In practical engineering, due to the machining accuracy issues during the manufacturing process of the charge module, there are certain errors in its key dimensional parameters. Therefore, the thickness, length, and outer diameter of the charge module are treated as random variables. At the same time, the performance parameters of the kraft paper material (such as tensile strength, elastic modulus) are affected by the consistency of the lamination process, humidity environment, and testing conditions, and there is also significant discreteness. Hence, the tensile strength and elastic modulus need to be treated as random variables. In addition, the external loads (such as shock acceleration, vibration spectrum, static load pressure) that the charge module withstands during transportation, storage, and launch are difficult to accurately quantify, and random variables are required to characterize its dynamic characteristics.

[0061] The above random variables are represented by the symbol X i (i = 1, 2,..., n). According to the kraft paper material performance test data, machining tolerance records, and measured load statistical results, an appropriate probability density function f(X i ) is selected for description, and its mean and variance are determined. Since the dimensional errors, material parameter fluctuations, and external loads are physically independent of each other, their joint probability density function can be expressed as the product of the probability density functions of each variable:

[0062] f X (X) = f X (X1, X2,..., X n ) = f1(X1)f2(X2)...f n (X n )

[0063] Step 3: Determination of the performance function:

[0064] The stiffness and strength failure performance functions of the charge module structure are respectively:

[0065] Z1 = g1(X) = r - Y1(X1, X2,..., X n )

[0066] Z2 = g2(X) = w - Y2(X1, X2,..., X n )

[0067] where Z1 is the strength failure performance function, Y1(X1, X2,..., X n ) is the stress response value, and r is the ultimate stress of the material; Z2 is the stiffness failure performance function, Y2(X1, X2,..., X n ) is the deformation response value, and w is the ultimate deformation value of the material. When the failure performance functions Z1 and Z2 are positive, it indicates that the structure is safe; when they are less than or equal to 0, it indicates that the structure is in a failure state.

[0068] Step 4: Determination of the optimization objective:

[0069] An excellent ton - level charge module structure should have the characteristics of low cost, easy processing, strong load - bearing capacity, high reliability, etc. Therefore, the optimization goal of the ton - level charge module is to achieve light weight and low cost on the premise of meeting strength reliability. The lower the weight of the charge module, the greater the mass of the explosive that can be loaded inside, and the explosion power of the whole ammunition is significantly improved. When the material selection has been determined as low - cost kraft paper (with a fixed density), to construct a high - reliability and light - weight ton - level charge module, the following key problems need to be solved: how to optimize the thickness, length, and outer diameter of the charge module structure to minimize the structure volume and weight, while ensuring that the load - bearing capacity reaches the ton - level and meets the strength reliability requirements. Based on this, the objective function of the light - weight design of the ton - level charge module can be defined as:

[0070] minW=ρ·π·L·((D + 2t) 2 - D 2 ) / 4

[0071] In the formula, W is the total weight of the charge module, ρ is the density of the kraft paper material, L is the length of the charge module, D is the outer diameter of the cabin, and t is the wall thickness of the cabin. The constraint conditions include reliability indexes such as the deformation of the module and the stress threshold, and their parameters can be obtained through ANSYS finite - element simulation.

[0072] Step 5: Determination of design variables:

[0073] The weight of the ton - level charge module is directly related to the structural processing dimensions. For its cylindrical cabin structure, the core geometric parameters that need to be clarified in the design are the cabin length, wall thickness, and outer diameter. Since the density of the low - cost kraft paper is fixed, to minimize the structure volume and weight while meeting the ton - level load - bearing requirements, the cabin length L, wall thickness t, and outer diameter D are finally selected as the key design variables.

[0074] Step 6: Determination of the target reliability:

[0075] In the structural reliability design, it is necessary to pre - define the target reliability index, which is usually determined based on the functional requirements and safety standards of the module. According to the overall reliability distribution scheme of the ammunition system, the target reliability of the load - bearing strength that the ton - level charge module needs to meet is R * , and the corresponding reliability indexes (such as the allowable stress threshold and the maximum deformation limit) need to be jointly calibrated through ANSYS finite - element simulation and material mechanics tests to ensure that the structure does not fail under the ton - level load.

[0076] Step 7: Construction of the light - weight design model based on reliability:

[0077] The reliability-based lightweight design needs to be constructed through probabilistic constraints to ensure that the optimization results meet both the requirements of structural lightweight and reliability. Different from the deterministic optimization that only focuses on the "theoretical optimal solution" within the feasible region of design variables, this model fully considers the uncertainty effects of design variables and parameter variables (including fluctuations in material property parameters, discreteness of yield limits, Poisson's ratio deviations, and dynamic distributions of explosive shock loads, etc.), and avoids the risk of structural failure caused by parameter randomness. Since deterministic optimization ignores the variable uncertainty, its design results may not meet the actual reliability constraints (such as degradation of the interlaminar shear strength of kraft paper and deviation of the critical threshold of large deformation buckling). Therefore, the lightweight design model constructed in this invention is as follows:

[0078] minW=ρ·π·μ L ·((μ D +2μ t ) 2 -μ D 2 ) / 4

[0079] L,t,D

[0080] R σ ≥R*

[0081] R δ ≥R*

[0082] R λ ≥R*

[0083] D min ≤D≤D max ,t min ≤t≤t max ,L min ≤L≤L max

[0084] Among them, W is the mass objective function, and μ L μ D μ t are the average values of the length, outer diameter, and wall thickness of the structure respectively. The subscripts min and max represent the upper and lower limits of the value ranges of these physical quantities respectively.

[0085] Step 8: Optimization solution for lightweight design:

[0086] In the traditional lightweight design model, the solution needs to be achieved through a two-layer nested optimization of outer-layer design variable optimization and inner-layer reliability analysis. However, the explosion shock simulation calculation based on ANSYS parametric modeling is time-consuming significantly, and the nested optimization will lead to a sharp decline in computational efficiency (the time-consuming for a single iteration can reach several hours). Therefore, the present invention proposes a sequential decoupling collaborative optimization method, and the core strategy is to decompose the nested optimization into an alternating iterative process of deterministic design optimization and reliability analysis: 1 Deterministic optimization stage: Convert the probabilistic constraints (strength, stiffness, stability reliability) into equivalent deterministic constraints; 2 In the reliability analysis stage, based on the current design scheme, use the Latin hypercube sampling-response surface proxy model (LHS-RSM) to quickly identify the most probable point of failure (MPP), and jointly correct the constraint boundary through a small amount of high-precision simulation and drop hammer impact test data to provide equivalent conversion parameters for the next iteration; 3 The two stages of the closed-loop iteration are alternately executed until convergence, and the total computational amount is reduced by 60%-80% compared with the nested optimization, which is especially suitable for the low-cost and rapid iteration requirements of kraft paper-based composite laminates.

[0087] The core of this decoupling method lies in the high-precision equivalent mapping of probabilistic constraints to deterministic constraints to balance computational efficiency and reliability requirements. Its essence is to balance computational efficiency and reliability accuracy by constructing a dynamic moving vector to correct the constraint boundary. Taking the double random variables (yield strength X1, interlaminar shear modulus X2) of kraft paper-based composites under explosion shock scenarios as an example, its equivalent process can be described as follows: On the basis of the deterministic constraint boundary g i (μ X ) = 0 (such as the allowable stress threshold), considering the material property dispersion (X1, X2 ∼ N(μ, σ 2 )) and the randomness of the explosion load, the actual probabilistic constraint boundary g i R (X) = 0 will shrink towards the inside of the feasible region to ensure that the structure meets the target reliability R * .

[0088] To achieve the equivalent mapping of probabilistic constraints to deterministic constraints, it is necessary to perform a reverse search through the most probable point of failure (MPP) in the reliability analysis stage to calculate the normal offset Δd (k) of the probabilistic constraint boundary relative to the deterministic boundary. This offset is jointly determined by the reliability index β (k) and the gradient modulus of the constraint function, that is where β (k) is dynamically calibrated through drop hammer impact test data and Monte Carlo joint simulation to avoid the conservative deviation caused by pure theoretical assumptions.

[0089] In the k-th iteration, the probabilistic constraint P(g i (X) ≤ 0) ≤ Φ(-β *) is converted into a deterministic constraint g i (k) (μ X ) = g i (μ X ) + Δd (k) ≤0 to form a deterministic optimization model that can be solved quickly. This model aims to minimize the structural weight W = ρπDtL, and the design variables D, t, L are subject to double constraints of the equivalent constraint boundary and the process dimension limit. By alternately performing deterministic optimization and reliability analysis, the total computational amount is reduced by 60%-80% compared with the traditional nested optimization, which is especially suitable for the low-cost and rapid iteration requirements of kraft paper laminated structures under explosion loads.

[0090] Step 9: Determine the optimal design:

[0091] Through the lightweight structure design in Step 6 and Step 7, the goal of minimizing the weight of the charge module housing can be achieved, and the optimal parameter combination of the thickness t, length L, and outer diameter D can be output synchronously. Based on the above optimization method, the present invention also provides a low-cost design device for a charge module based on reliability, whose hardware architecture includes a memory and a processor, where the memory pre-stores a kraft paper material property database (including statistical distribution data of elastic modulus and tensile strength), and an ANSYS APDL script template; the processor is configured to automatically execute finite element parametric modeling, buckling analysis, and Monte Carlo reliability assessment by calling the script, and finally generate a Pareto optimal solution set that satisfies σ max ≤0.8σ 牛皮纸 . By running the optimization program through this device, under the condition of ensuring that the bearing capacity of the housing is in the ton level, the manufacturing cost per unit length can be reduced to 17% of the traditional metal structure, and at the same time, the internal charge space volume can be increased by 9.2%.

[0092] Example 1

[0093] A method for designing a combustible cartridge structure for ton-level charges based on reliability, the specific steps are as follows:

[0094] Structure modeling: According to the usage requirements of the ton-level charge cartridge, establish a structural model of the cartridge, considering the geometric shape, material properties, and external loads of the structure.

[0095] Reliability analysis: Adopt reliability analysis methods (such as Monte Carlo simulation, reliability sensitivity analysis, etc.) to conduct reliability assessment on the cartridge structure, and evaluate the failure probability of the cartridge under different usage conditions.

[0096] Optimization goal setting: The structural weight is used as the optimization goal, and at the same time, performance indicators such as the strength and stiffness of the cartridge are used as design constraint conditions.

[0097] Optimization Algorithm Selection: Select appropriate structural optimization algorithms, such as genetic algorithms, particle swarm optimization, etc., to optimize the structural size parameters and ensure the minimum weight of the cartridge while meeting the strength constraints.

[0098] Reliability Design: Based on the results of reliability analysis, adjust the design to improve the anti-failure ability of the structure and ensure that it can achieve the required reliability level under various environments and usage conditions.

[0099] Implementation Details: Include details such as material selection, precise dimension setting of the structure, and realization of the optimized design.

[0100] Key Technology Verification:

[0101] Material Preparation: Use the TDOE process (temperature 180°C, pressure 0.8 MPa, vacuum degree ≤ 5×10-3 Pa) to achieve uniform dispersion of nanofibers, and the interlayer bonding strength is increased by 40% as detected by XRD.

[0102] Reliability Testing: Completed by the MIL-STD-810H method:

[0103] High Temperature Test: Keep at a constant temperature of 85°C for 72 hours, and the retention rate of interlayer shear strength ≥ 92%. Low Temperature Test: 100 times of thermal shock at -55°C, no cracking phenomenon.

[0104] Vibration Test: Random vibration in three XYZ axes for 8 hours each, mass loss ≤ 0.05%.

Claims

1. A low-cost, reliability-based, tonnage-class charge module structural design, characterized in that: include: The core geometric parameters of the charge module structure are used as design variables, including module shell length, wall thickness and outer diameter, and the parametric design optimization model is constructed with the minimization of module structure weight as the optimization goal. Taking the reliability of explosion resistance, impact stiffness and buckling stability of the charge module as multiple constraints, a lightweight design model is constructed based on the probabilistic reliability theory, in which the target reliability threshold is calibrated jointly by finite element transient dynamics simulation and dynamic impact test. The sequential decoupling optimization method is adopted to decompose the nested optimization into an alternating iterative process of deterministic design optimization and reliability analysis. In the deterministic optimization stage, the probabilistic constraints are equivalent to deterministic constraints through the dynamic boundary shrinkage strategy. During the reliability analysis phase, the key failure modes are quickly located based on the sampling-surrogate model, and the constraint boundary offset is corrected in combination with the material property dispersion data until the convergence conditions are met. Finally, a lightweight geometric parameter combination that meets the explosion load bearing requirements is output.

2. The reliability-based lightweight design method for airborne external storage compartment structure according to claim 1 is characterized in that: The structural dimension parameters include module shell thickness, length and outer diameter; The establishing of the design optimization model comprises: According to the geometric characteristics and structural size parameters of the charge module structure, a mathematical representation model of the module shell volume is constructed; Taking minimizing the module housing volume as the optimization goal, the structural dimension parameters are iteratively optimized.

3. The reliability-based lightweight design method for an airborne external storage compartment structure according to claim 2 is characterized in that: To build a lightweight model, the steps include: Determine the key random variables based on the manufacturing tolerance of the charge module structure, the fluctuation characteristics of material properties and the dynamic distribution characteristics of the explosion impact load; According to the measured material performance data and load spectrum statistical results, the probability distribution model of random variables is selected; Combined with the explosion resistance performance requirements of the charge module, the module shell strength and stiffness response function is constructed with random variables as input; Through the fusion modeling of finite element transient impact simulation and drop hammer test data, a proxy model of stress and deformation response is generated, and a stratified sampling method is used in the random variable space to obtain a sample set, and the stress distribution and deformation characteristics of the module shell are calculated based on high-fidelity simulation; A surrogate model of stress-deformation response is constructed based on Gaussian process regression to quantify the correlation deviation characteristics between random variable samples; According to the ultimate shear strength and explosion-proof deformation threshold of the charge module shell material, combined with the stress-deformation response containing uncertain parameters, the strength failure criterion and the stiffness failure criterion are defined respectively. A lightweight design model is constructed with the constraint that both the strength failure criterion and the stiffness failure criterion meet the target reliability threshold.

4. The reliability-based lightweight design method for charge module structure according to any one of claims 1 to 3, characterized in that: Sequence decoupling optimization methods include: Design optimization stage: convert the probabilistic reliability constraints into equivalent deterministic constraints, construct a deterministic optimization problem through a dynamic boundary shrinkage strategy, and solve it to obtain a preliminary lightweight design solution; Reliability analysis stage: Based on the explosion impact load spectrum and material performance dispersion data, identify the maximum possible failure point of the design scheme in key modes such as interlayer shear failure and buckling instability, and correct the constraint boundary offset through drop hammer test data feedback; Iterative collaborative mechanism: The gradient information of the maximum possible failure point and the statistical characteristics of material fluctuations are fed back to the next round of design optimization, and the equivalent constraint boundaries are dynamically adjusted until the target reliability threshold and weight convergence conditions are met, thereby achieving the collaborative optimization of the anti-explosion performance and lightweight goals of the charge module.

5. The reliability-based lightweight design method for charge module structure according to claim 4 is characterized in that: Obtain a design plan, the steps include: Based on the constraint boundary of the previous iteration, a dynamic boundary offset is applied to the feasible region of the charge module structure to generate the probability constraint equivalent approximate boundary of the current iteration as the constraint condition of this design scheme; A deterministic optimization model was constructed, whose objective function was to minimize the weight of the module shell. The constraints were the shear strength boundary and buckling stability boundary after equivalent shrinkage. A parametric optimization algorithm was used to solve the lightweight geometric parameter combination that met the explosion impact scenario. The dynamic boundary offset is calibrated jointly by material property dispersion data and the random component of the explosion load, and the engineering rationality of the offset direction is verified by a drop hammer test.

6. The reliability-based lightweight design method for charge module structure according to claim 1 is characterized in that: The optimization phase consists of three innovation phases: Multi-scale modeling stage: construct a three-dimensional nested model from molecular dynamics MD to macroscopic finite element FE to achieve accurate mapping of material microstructure and macroscopic mechanical behavior; Digital twin verification phase: Develop an automated verification platform based on ANSYS+Python, integrate the real-time data acquisition interface and machine learning prediction module, and set the sampling rate ≥ 1kHz; Intelligent iteration stage: The improved quantum particle swarm algorithm QPSO is used, with a population size of 500, number of iterations of 2000, and an adaptive inertia weight dynamic adjustment mechanism of 0.6-0.9.

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