Structural Design Models Research Articles

Reinforcement structure design and matrix model establishment of tubular 3D weaving based on ordinary loom

In order to optimize the design of three-dimensional tubular woven (3DTW), a design method and matrix model of 3DTW were proposed based on normal loom, where 3D woven was used as tube wall and the weaving method of tubular fabric was applied. Herein, 3D woven was used as the tube wall to obtain the face weave diagram, and the back weave diagram was subsequently obtained by the “negative flip” method. According to the method of layering weaving, the structure diagram of 3DTW could be determined. In order to obtain back weave matrix, the elements in the face weave matrix were replaced and reordered by MATLAB function, and Kronecker product operation was used to achieve the proportional embedding of the face and the back weave matrix and the assignment of the face warp by lifting point elements when weaving the back weft. Finally, the weave matrix of 3DTW was obtained. The proposed design method and matrix model can improve the design efficiency and reduce weaving cost of 3DTW, which could provide a reference for the design and preparation of 3DTW.

Optimal design of laminated thermoelectric cylindrical device based on neural network and thermo-electric-mechanical coupled model

The laminated thermoelectric cylindrical shell is favorable for solar thermal energy harvesting, but the thermo-electric-mechanical coupling field is relatively complex. An optimization design model of thermoelectric structure is required to promote its practical application. In this paper, based on variable separation method, thermo-electric-mechanical coupled model of laminated thermoelectric cylindrical device under thermal shock is constructed. Transient temperature field, thermal stress and power generation efficiency are obtained. Numerical results show that both power generation efficiency and thermal stress are affected by material parameters simultaneously. In order to analyze efficiency and structural reliability comprehensively, a fitness function is introduced in this paper. Based on the data which are collected from thermoelectric coupled model, a neural network is established and trained, which can predict and evaluate the performance of thermoelectric cylindrical shell based on the fitness function. The optimal material parameters for thermoelectric shells with high efficiency and low thermal stress are obtained. Combining neural network technique and the theoretical model developed in this paper, the computation time of numerical results can be reduced from 50 to 3 min, and this neural network optimization method has higher computational efficiency. These research results will provide guidance for the optimization design of cylindrical thermoelectric device.

Research on energy consumption evaluation and energy-saving design of cranes in service based on structure-mechanism coupling

Aiming at the problem that the life cycle energy consumption index of hoisting machinery is difficult to be objectively quantified, a crane energy consumption evaluation calculation method based on the working cycle of the service period is proposed, the typical service working mode of the crane is analyzed, and the working cycle process of the mechanism is clarified. On this basis, the stress cycle characteristics of the crane structure during the service period are determined based on the coupling relationship between the mechanism and the structure, which lays the foundation for accurately evaluating the life index of the crane. According to the relationship between the load rate and effective power of each mechanism during the service period of the crane, the calculation method for the evaluation and calculation of the structure energy consumption during the service period of the crane is studied. Finally, the crane is designed based on the concept of absolute service safety, and an optimal design model of the main girder structure of the crane with the goal of the lowest energy consumption in service is established. Using the research method of this paper to study a certain type of bridge crane, the energy consumption of the hoisting machinery during its service period is not only related to the accumulation of the operating characteristics of the mechanism but also related to the characteristic parameters such as the structure self-weight. The comprehensive evaluation of energy consumption provides an effective quantitative method and a reliable green design theory for crane design.

An analytical formula evolved from thermal-structural coupling model for life prediction and structural design of reusable thrust chambers

The thermal-structural analytic model is essential for the life prediction of reusable rocket thrust chambers. The common numerical model based on the finite element method is computationally intensive. In order to simplify the life prediction method and improve the calculation efficiency, a thermal-structural analytical formula is proposed. The one-dimensional analytical heat transfer algorithm for accurately calculating the temperature of the thrust chamber is established, and Porowski’s beam model for calculating the structural deformation is adopted. Finally, the analytic formula for life prediction is directly established by reasonable simplification. This is an algebraic solution describing the relationship between life and the cooling channel parameters, and the computational efficiency is improved by about 12,000 times. The calculated temperature and deformation of the inner wall are verified by the finite element method with an error of less than 10 %, indicating the proposed analytical formula is accurate. Further, the analytical formula is applied to design the cooling channel parameters according to the given target life. The structural design method is validated by numerical simulation and experimental data. The analytical formula and the structural design method provide new ideas for the analysis and design of reusable rocket engines, which have broad application prospects.

A multi-objective optimization design method for underwater composite sandwich structures with high acoustic transmission and low vibration performance

ABSTRACT To address the issue of reduced structural integrity and optimality resulting from the conventional serial design of sonar dome structures, this paper establishes a multi-objective optimal design model for composite sandwich structures, utilising the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The model's stability and reliability are subsequently validated. Upon this model, a structural optimization design technique is postulated for composite sandwich structures within the realm of Fluid-Structure Interaction (FSI) multi-physics coupling. This approach aims to optimize the lightweight, high-rigidity composite sandwich structure, enhancing its acoustic transmission and minimizing vibration performance under turbulent pressure pulsations. The suggested method notably enhances the efficiency of structural design while ensuring precision. The proposed method effectively addresses the deficiency of serially designed sonar domes by neglecting the synergistic effects among multiple disciplines. In summary, this approach offers technical support and new research perspectives for the design of sonar domes under the coupling of multiple physical fields.

MULTI-OBJECTIVE GREEN DESIGN MODEL BASED ON COSTS, CO2 EMISSIONS AND SERVICEABILITY FOR HIGH-RISE BUILDINGS WITH A MEGA-STRUCTURE SYSTEM

In light of growing environmental concerns, the reduction of CO2 emissions is increasingly vital. Particularly in the construction industry, a major contributor to global carbon emissions, addressing this issue is critical for environmental sustainability and mitigating the accelerating impacts of climate change. This study proposes the Optimal Green Design Model for Mega Structures (OGDMM) to optimise CO2 emissions, cost-effectiveness, and serviceability in highrise buildings with mega structures. The OGDMM examines the impact of each material and structural design of main members on these three critical aspects. Analytical results for high-rise buildings (120–200 m, slenderness ratio: 2.0–8.0) demonstrate that OGDMM can reduce CO2 emissions and costs by an average of 4.67% and 3.97%, respectively, without compromising serviceability. To ensure comprehensive evaluation, this study introduces five new evaluation indicators encompassing environmental, economic, and serviceability performances of high-rise buildings. Based on these criteria, optimised structural designs for high-rise buildings are classified into four categories according to slenderness ratio, leading to the formulation of corresponding design guidelines. The model’s applicability is further validated through its application to a 270-m-tall high-rise building in Korea, showing reductions in CO2 emissions and costs by 8.99% and 18.50%, respectively, while maintaining structural serviceability.

High strain rate response of ABS-M30i-based 3D printed, bio-inspired, bovine bone structure

To investigate osteoporosis caused by aging and the dynamic behavior of male bovine trabecular bone, three age groups of male bovine trabecular bone were chosen, and micro-computed tomography (CT) analysis was performed to develop an image-based bio-inspired computer-aided design (CAD) model of the bone structure. Further experimental and computational studies were carried out to examine the rate-dependent behavior and compressive energy-absorbing capacity of the structure as a function of age. To evaluate this study, a micro-CT-based CAD model of the structure was 3D printed using ABS-M30i material and subjected to quasi-static compression (low strain rate) and high strain rate (split Hopkinson pressure bar) compression. The findings show that 3D-printed bovine structures have distinct high-rate dependence at strain rates greater than 430 s−1, as well as sensitivity to strain rate in terms of peak stress, plateau stress, and energy absorption capacity. Using rate-dependent properties, the Johnson–Cook damage plasticity model was used in computational analysis to explain the dynamic behavior of bone due to osteoporosis. Overall, there is good agreement between the numerical simulations and the experimental data, which was obtained by verifying and validating the model against the experimental results.

Innovative Three-Row Pile Support System of Ultra-Deep Foundation Pit and Cooperative Construction Technology with Basement for High-Rise Tower Structures

This paper proposes the structural design and calculation model of stepped three-row pile and verifies its antioverturning and antisliding stability, based on the Xinghe Yabao deep foundation pit project in Shenzhen, China. The three-row pile model is constructed using finite element software, and the force and deformation of the piles are analyzed. The influence of the direction of the prestressing anchors on the support effect of the three-row pile is investigated by simulating the prestressing anchors in three directions: oblique, horizontal, and vertical. The results show that the combined support effect of the oblique anchor and the three-row pile is the most effective, resulting in the smallest deformation, followed by the horizontal anchor, and the vertical anchor produces the largest deformation. Finally, the innovative construction method of the three-row pile removal and the basement top-down construction method is proposed. The three-dimensional model of the tower building and the basement is established by the finite element software to simulate the structural grading load and the cooperative construction of the supporting structure and the basement structure. The research results have greater engineering application value and significant economic benefits, which could provide reference and guidance for the design and construction of deep foundation pits in similar projects.

Dynamic Sub-graph Distillation for Robust Semi-supervised Continual Learning

Continual learning (CL) has shown promising results and comparable performance to learning at once in a fully supervised manner. However, CL strategies typically require a large number of labeled samples, making their real-life deployment challenging. In this work, we focus on semi-supervised continual learning (SSCL), where the model progressively learns from partially labeled data with unknown categories. We provide a comprehensive analysis of SSCL and demonstrate that unreliable distributions of unlabeled data lead to unstable training and refinement of the progressing stages. This problem severely impacts the performance of SSCL. To address the limitations, we propose a novel approach called Dynamic Sub-Graph Distillation (DSGD) for semi-supervised continual learning, which leverages both semantic and structural information to achieve more stable knowledge distillation on unlabeled data and exhibit robustness against distribution bias. Firstly, we formalize a general model of structural distillation and design a dynamic graph construction for the continual learning progress. Next, we define a structure distillation vector and design a dynamic sub-graph distillation algorithm, which enables end-to-end training and adaptability to scale up tasks. The entire proposed method is adaptable to various CL methods and supervision settings. Finally, experiments conducted on three datasets CIFAR10, CIFAR100, and ImageNet-100, with varying supervision ratios, demonstrate the effectiveness of our proposed approach in mitigating the catastrophic forgetting problem in semi-supervised continual learning scenarios. Our code is available: https://github.com/fanyan0411/DSGD.

Machine learning for structural design models of continuous beam systems via influence zones

Abstract This work develops a machine learned structural design model for continuous beam systems from the inverse problem perspective. After demarcating between forward, optimisation and inverse machine learned operators, the investigation proposes a novel methodology based on the recently developed influence zone concept which represents a fundamental shift in approach compared to traditional structural design methods. The aim of this approach is to conceptualise a non-iterative structural design model that predicts cross-section requirements for continuous beam systems of arbitrary system size. After generating a dataset of known solutions, an appropriate neural network architecture is identified, trained, and tested against unseen data. The results show a mean absolute percentage testing error of 1.6% for cross-section property predictions, along with a good ability of the neural network to generalise well to structural systems of variable size. The CBeamXP dataset generated in this work and an associated python-based neural network training script are available at an open-source data repository to allow for the reproducibility of results and to encourage further investigations.

High flame retardant composite phase change materials with triphenyl phosphate for thermal safety system of power battery module

The thermal safety of battery pack has attracted much attention accompany with the growth in electric vehicles (EVs) in recent years. Although various battery thermal management systems (BTMS) are investigated by many research, the thermal runaway propagation (TRP) of battery packs under extremely abused conditions is just at the level of structural design and theoretical model. How to explore an innovative technology to improve the integrated thermal safety including the BTMS and TRP is still a great challenge. In this study, a multifunctional flame-retardant paraffin (PA)/styrene-butadiene-styrene (SBS)/expanded graphite (EG)/methylphenyl silicone resin (MPS)/triphenyl phosphate (TPP) composite phase change material (PSEMT) has successfully prepared. Besides, it has applied in 26650 ternary power battery modules. When the proportion of MPS and TPP is 1:2, the experimental results reveal that PSEMT possesses high thermal stability, and excellent flame-retardant properties owing to synergistic flame-retardant effect with phosphorus and silicon. Further, the cylindrical 26650 battery module with PSEMT exhibits optimum thermal management performance. Even at 2C discharge rate after ten cycles, the maximum operating temperature of battery module can still be maintained below 50 °C, and the maximum temperature difference is controlled within 4.6 °C. Additionally, it displays an excellent thermal runaway suppression through triggering by multiple heat sources. What's more, the battery with PSEMT can suppress the peak temperature and delay the occurrence time of thermal runaway. Therefore, it can be induced that the battery module with PSEMT can effectively avoid heat accumulation and significantly reduce its thermal safety risk. This study offers a new solution with promising prospects from the perspectives of energy storage and EVs, for balancing the temperature inconsistencies in batteries and suppressing thermal runaway in the battery packs.

Design and Control of Upper Limb Rehabilitation Training Robot Based on a Magnetorheological Joint Damper.

In recent years, rehabilitation robots have been developed and used in rehabilitation training for patients with hemiplegia. In this paper, a rehabilitation training robot with variable damping is designed to train patients with hemiplegia to recover upper limb function. Firstly, a magnetorheological joint damper (MR joint damper) is designed for the rehabilitation training robot, and its structural design and dynamic model are tested theoretically and experimentally. Secondly, the rehabilitation robot is simplified into a spring-damping system, and the rehabilitation training controller for human movement is designed. The rehabilitation robot dynamically adjusts the excitation current according to the feedback speed and human-machine interaction torque, so that the rehabilitation robot always outputs a stable torque. The magnetorheological joint damper acts as a clutch to transmit torque safely and stably to the robot joint. Finally, the upper limb rehabilitation device is tested. The expected torque is set to 20 N, and the average value of the output expected torque during operation is 20.02 N, and the standard deviation is 0.635 N. The output torque has good stability. A fast (0.5 s) response can be achieved in response to a sudden motor speed change, and the average expected output torque is 20.38 N and the standard deviation is 0.645 N, which can still maintain the stability of the output torque.

A Modified Horse Herd Optimization Algorithm and Its Application in the Program Source Code Clustering

Maintenance is one of the costliest phases in the software development process. If architectural design models are accessible, software maintenance can be made more straightforward. When the software’s source code is the only available resource, comprehending the program profoundly impacts the costs associated with software maintenance. The primary objective of comprehending the source code is extracting information used during the software maintenance phase. Generating a structural model based on the program source code is an effective way of reducing overall software maintenance costs. Software module clustering is considered a tremendous reverse engineering technique for constructing structural design models from the program source code. The main objectives of clustering modules are to reduce the quantity of connections between clusters, increase connections within clusters, and improve the quality of clustering. Finding the perfect clustering model is considered an NP-complete problem, and many previous approaches had significant issues in addressing this problem, such as low success rates, instability, and poor modularization quality. This paper applied the horse herd optimization algorithm, a distinctive population-based and discrete metaheuristic technique, in clustering software modules. The proposed method’s effectiveness in addressing the module clustering problem was examined by ten real-world standard software test benchmarks. Based on the experimental data, the quality of the clustered models produced is approximately 3.219, with a standard deviation of 0.0718 across the ten benchmarks. The proposed method surpasses former methods in convergence, modularization quality, and result stability. Furthermore, the experimental results demonstrate the versatility of this approach in effectively addressing various real-world discrete optimization challenges.

A novel method for fast computation of the temperature rise and optimal design of GIL based on thermal network model

Accurate and fast computation of the temperature rise of gas-insulated transmission lines (GIL) is of great significance to its efficient operation and optimal design. This study proposes a novel method for efficient and accurate computation of the temperature rise and optimal design of GIL based on the thermal network method (TNM). The proposed TNM considers different heat transfer processes between the upper and lower parts of the GIL and the corresponding thermodynamic differential equations are derived. When applied to numerical simulation of GIL, the TNM can save more than three orders of magnitude in computational time compared to computational fluid dynamics (CFD) simulations, and good agreement with experimental data is also achieved. Then a fast optimal design model for GIL current-carrying structures is established based on the TNM, where it is convenient to consider factors including temperature rise and cost objectives, as well as electrical and mechanical field constraints. This optimal design approach circumvents the highly time-consuming electromagnetic-thermal-fluid coupled numerical computations. It is concluded that the proposed TNM provides an accurate way for temperature rise calculation and optimal design of GIL with really high efficiency.

The mechanical deformation characteristics of the internal structure of large diameter shield tunnel in different construction stages

PurposeUnder the repeated action of the construction load, opening deformation and disturbed deformation occurred at the precast box culvert joints of the shield tunnel. The objective of this paper is to investigate the effect of construction vehicle loading on the mechanical deformation characteristics of the internal structure of a large-diameter shield tunnel during the entire construction period.Design/methodology/approachThe structural response of the prefabricated internal structure under heavy construction vehicle loads at four different construction stages (prefabricated box culvert installation, curved lining cast-in-place, lane slab installation and pavement structure casting) was analyzed through field tests and ABAQUS (finite element analysis software) numerical simulation.FindingsHeavy construction vehicles can cause significant mechanical impacts on the internal structure, as the construction phase progresses, the integrity of the internal structure with the tunnel section increases. The vertical and horizontal deformation of the internal structure is significantly reduced, and the overall stress level of the internal structure is reduced. The bolts connecting the precast box culvert have the maximum stress at the initial stage of construction, as the construction proceeds the stress distribution among the bolts gradually becomes uniform.Originality/valueThis study can provide a reference for the design model, theoretical analysis and construction technology of the internal structure during the construction of large-diameter tunnel projects.

Seismic isolation effect of tunable friction pendulum system in bridge

ABSTRACT A Tunable Friction Pendulum System (TFPS) was proposed to investigate its seismic isolation control performance in bridge structures. First, the structural design and theoretical model of TFPS are given. Second, the mechanical properties of TFPS are studied through finite element simulation. Then, the above TFPS is introduced into the bridge structure, and the analysis model is established based on SAP2000. Finally, a nonlinear time-history analysis is carried out on the bridge structure, and the seismic isolation performance of TFPS is evaluated by using the dynamic characteristic response index of the bridge structure. Theoretical derivation and finite element analysis results show that TFPS can adjust the dynamic characteristics in real time according to the spectral characteristics of the ground motion input and the response of the structure itself, so that the friction force can be continuously adjusted and energy consumption can be adjusted.

A finite element-convolutional neural network model (FE-CNN) for stress field analysis around arbitrary inclusions

This study presents a data-driven finite element-machine learning surrogate model for predicting the end-to-end full-field stress distribution and stress concentration around an arbitrary-shaped inclusion. This is important because the model’s capacity to handle large datasets, consider variations in size and shape, and accurately replicate stress fields makes it a valuable tool for studying how inclusion characteristics affect material performance. An automatized dataset generation method using finite element simulation is proposed, validated, and used for attaining a dataset with one thousand inclusion shapes motivated by experimental observations and their corresponding spatially-varying stress distributions. A U-Net-based convolutional neural network (CNN) is trained using the dataset, and its performance is evaluated through quantitative and qualitative comparisons. The dataset, consisting of these stress data arrays, is directly fed into the CNN model for training and evaluation. This approach bypasses the need for converting the stress data into image format, allowing for a more direct and efficient input representation for the CNN. The model was evaluated through a series of sensitivity analyses, focusing on the impact of dataset size and model resolution on accuracy and performance. The results demonstrated that increasing the dataset size significantly improved the model’s prediction accuracy, as indicated by the correlation values. Additionally, the investigation into the effect of model resolution revealed that higher resolutions led to better stress field predictions and reduced error. Overall, the surrogate model proved effective in accurately predicting the effective stress concentration in inclusions, showcasing its potential in practical applications requiring stress analysis such as structural engineering, material design, failure analysis, and multi-scale modeling.

Machine learning powered sketch aided design via topology optimization

Structural topology optimization is an important design tool in the conceptual design phase of a product. However, the current topology optimization design is mostly driven strictly based on mathematical and mechanical models. Although the innovative design can be automated, it mostly lacks effective manual experience guidance. To improve the efficiency of computer-aided structural design models, this work proposes a sketch-guided topology optimization approach based on machine learning. Using neural network-based style transfer techniques, computer-digitized/hand-drawn sketches are explicitly incorporated into topology optimization in the form of constraint functions. The obtained optimization results not only satisfy the requirements of optimal mechanical properties, but also fully demonstrate the design intention and requirements of designers. Numerical examples show that the proposed approach can effectively compensate for the lack of manual experience guidance for topology optimization.

Connecting performance evaluation of the Quick-Connector for modular steel construction

This study presents the Quick-Connector, a new self-fastening joint system designed specifically for modular steel construction. By eliminating the need for a dedicated workspace during the connection process, the Quick-Connector minimizes disruptions to finishing work. A comprehensive set of tests, including pull-out, shear, and cyclic tests, was conducted on a full-scale prototype to evaluate its structural performance. The experimental results demonstrate that the Quick-Connector exhibits exceptional connecting performance, meeting the shear and tensile strength requirements for high-tension bolts. The seismic performance of the Quick-Connector was also evaluated through cyclic testing, highlighting its potential as a viable replacement for high-tensile bolts. Furthermore, a push-over analysis was carried out to develop an elastic structural design model specifically for buildings incorporating the Quick-Connector. The analysis results closely aligned with the experimental findings, validating the accuracy of the proposed model. Overall, the Quick-Connector offers a promising alternative to traditional bolted connections in modular construction, with the potential to enhance construction efficiency, quality, and safety.

Research on a demand design method of a cross polarization converter metasurface based on a depth generation model

As a two-dimensional electromagnetic metamaterial, the cross-polarization conversion (CPC) metasurface is thin, easy to develop, and has attracted wide attention. However, existing CPC cell surface designs still rely on inefficient full-wave numerical simulation. Although some researchers have explored deep learning CPC metasurface structure design methods, the generated metasurface patterns are of poor quality. In this paper, an on-demand design method for cross-polarization conversion metasurface based on depth-generation model is proposed. Firstly, Wasserstein generative adversarial network (WGAN) is used to reverse design CPC metasurface, and Wasserstein distance is introduced to replace JS divergence and KL divergence to optimize the target. The problem of training difficulty caused by gradient elimination of original generative adversarial network (GAN) is fundamentally solved. Secondly, in the WGAN model, U-Net architecture generator is used to generate images, which greatly improves the surface image quality of CPC. In addition, a simulator composed of convolutional neural network (CNN) is also added in this paper to carry out forward prediction of S-parameter spectrum diagram. By inputting the patterns generated by WGAN into the simulator, the corresponding S-parameter spectrum diagram is generated and compared with the real S-parameter spectrum diagram, so as to verify whether the surface patterns of generated elements meet the requirements. The depth generation model proposed in this paper organically combines the forward spectrum prediction model and the reverse CPC metasurface structure design model, so that the CPC metasurface structure satisfying the expected electromagnetic response can be designed quickly on demand. This on-demand design method is expected to promote the rapid design, fabrication and application of electromagnetic devices.