Substructure Method Research Articles

Numerical simulation method for shock vibration based on finite element substructure

Abstract For engineering problems such as shock vibration, finite element simulation is a very effective method, but for too large models, meshing them in finite element software generates a large number of grid cells, which is time-consuming to compute and limits the application of solid cells in the simulation of large models, so the use of the substructure method will effectively solve the problem. In this paper, we will first explore the influence of the mesh size on the simulation results to get the most suitable mesh. This paper will first investigate the effect of grid size on the simulation results to get the most appropriate grid size, and then use the substructure method to encrypt the grid of the part that needs to be studied and analyze it individually, and at the same time, carry out the validation experiments to compare the results with the substructure simulation results, to investigate the reasonableness of the substructure method in the propagation process of shock vibration.

Analysis of the influence of clamp installation position on vibration stress for spatial pipeline

In aero-engines, small changes in the clamp position can sometimes significantly alter the vibration stress of the pipeline system, so there is an urgent need to study the influence of clamp position on pipeline vibration stress. Firstly, a dynamic modeling method of spatial pipeline system is proposed based on the finite element method, the modeling method can more accurately simulate the complex boundary constraints caused by multiple clamps and pipe fittings. Then, an improved dynamic substructure method (reduced-order method) is developed by combining the dynamic substructure method with the clamp position parametric model to improve the efficiency of the subsequent clamp position parameters influence analysis. Further, the rationality and efficiency of the reduced-order modeling approach are verified by numerical and experimental studies. Finally, the influence of the clamp position and the key parameters of different components (pipe body, clamps, fittings) on the pipeline vibration stress is investigated. The results show that when the main vibration region of the pipeline system coincides with the installation region of the clamp, the vibration stress can be significantly reduced by installing the clamp in the maximum modal displacement region of the corresponding pipe segment. The related modeling methods and conclusions can provide valuable references for the dynamics design of pipeline system in engineering practice.

Modular Modeling of a Half-Vehicle System Using Generalized Receptance Coupling and Frequency-Based Substructuring (GRCFBS)

This paper presents an advanced modular modeling approach for vertical vibration analysis of dynamic systems using the Generalized Receptance Coupling and Frequency-Based Substructuring (GRCFBS) method. The focus is on a four-DoF half-vehicle model comprising three key subsystems: front suspension, rear suspension, and the vehicle’s trimmed body. The proposed technique is designed to predict dynamic responses in reconfigurable systems across various applications, including automotive, robotics, mechanical machinery, and aerospace structures. By coupling the receptance matrices (FRFs) of individual vehicle modules, the overall system receptance matrix is efficiently derived in a disassembled configuration. Two generalized coupling methods, originally developed by Jetmundsen and D.D. Klerk, are employed to determine the complete vehicle’s receptance matrix from its subsystems. Validation is achieved by comparing the results with established methods, such as direct solution and modal analysis, demonstrating high accuracy and reliability for complex dynamic systems. This modular approach allows for the creation of reduced-order models focused on key measurement points without the need for detailed system representation. The method offers significant advantages in early-stage vehicle development, providing critical insights into system vibration behavior.

Reduced-Order Modeling for Dynamic System Identification with Lumped and Distributed Parameters via Receptance Coupling Using Frequency-Based Substructuring (FBS)

Paper presents an effective technique for developing reduced-order models to predict the dynamic responses of systems using the receptance coupling and frequency-based substructuring (RCFBS) method. The proposed approach is particularly suited for reconfigurable dynamic systems across various applications, like cars, robots, mechanical machineries, and aerospace structures. The methodology focuses on determining the overall system receptance matrix by coupling the receptance matrices (FRFs) of individual subsystems in a disassembled configuration. Two case studies, one with distributed parameters and the other with lumped parameters, are used to illustrate the application of this approach. The first case involves coupling three substructures with flexible components under fixed–fixed boundary conditions, while the second case examines the coupling of subsystems characterized by multiple masses, springs, and dampers, with various internal and connection degrees of freedom. The accuracy of the proposed method is validated against a numerical finite element analysis (FEA), direct methods, and a modal analysis. The results demonstrate the reliability of RCFBS in predicting dynamic responses for reconfigurable systems, offering an efficient framework for reduced-order modeling by focusing on critical points of interest without the need to account for detailed modeling with numerous degrees of freedom.

A mechanics-based data-free Problem Independent Machine Learning (PIML) model for large-scale structural analysis and design optimization

Machine learning (ML) enhanced fast structural analysis and design recently attracted considerable attention. In most related works, however, the generalization ability of the ML model and the massive cost of dataset generation are the two most criticized aspects. This work combines the advantages of the universality of the substructure method and the superior predictive ability of the operator learning architecture. Specifically, using a novel mechanics-based loss function, lightweight neural network mapping from the material distribution inside a substructure and the corresponding continuous multiscale shape function is well-trained without preparing a dataset. In this manner, a problem machine learning model (PIML) that is generally applicable for efficient linear elastic analysis and design optimization of large-scale structures with arbitrary size and various boundary conditions is proposed. Several examples validate the effectiveness of the present work on efficiency improvement and different kinds of optimization problems. This PIML model-based design and optimization framework can be extended to large-scale multiphysics problems.

Fungicidal Activity of Novel 6-Isothiazol-5-ylpyrimidin-4-amine-Containing Compounds Targeting Complex I Reduced Nicotinamide Adenine Dinucleotide Oxidoreductase.

To discover novel inhibitors of the complex I reduced nicotinamide adenine dinucleotide (NADH) oxidoreductase as fungicides, a series of 6-isothiazol-5-ylpyrimidin-4-amine-containing compounds were designed using a computer-aided pesticide design method and splicing of substructures from diflumetorim and isotianil. In vitro fungicidal bioassays indicated that compounds T17-T24 showed high inhibitory activity against Rhizoctonia solani with an effective concentration (EC50) value falling between 2.20 and 23.85 μg/mL, which were more active than or equivalent to the lead diflumetorim with its EC50 of 19.80 μg/mL. In vivo antifungal bioassays demonstrated that, at a concentration of 200 μg/mL, T7 and T21 showed higher inhibition against Pseudoperonospora cubensis than all other compounds, while T23 exhibited the highest inhibition against Sphaerotheca fuliginea. T23 showed an approximately twofold lower inhibition potency against R. solani complex I NADH oxidoreductase than diflumetorim. Molecular docking and transcriptomic analyses indicated that T23 and diflumetorim both might share the same mode of action, targeting NADH oxidoreductase. T23 as a good fungicidal candidate against R. solani is worthy of further investigation.

Implementation of PMDL and DRM in OpenSees for Soil-Structure Interaction Analysis

It is widely acknowledged that the effects of soil-structure interaction (SSI) can have substantial implications during periods of intense seismic activity; therefore, accurate quantification of these effects is of paramount importance in the design of earthquake-resistant structures. The analysis of SSI is typically conducted using either direct or substructure methods. Both of these approaches involve the use of numerical models with truncated or reduced-order computational domains. To ensure effective truncation, it is crucial to employ boundary representations that are capable of perfectly absorbing outgoing waves and allowing for the consistent application of input motions. At present, such capabilities are not widely available to researchers and practicing engineers. In order to address this issue, this study implemented the Domain Reduction Method (DRM) and Perfectly Matched Discrete Layers (PMDLs) in OpenSees. The accuracy and stability of these implementations were verified through the use of vertical and inclined incident SV waves in a two-dimensional problem. In terms of computational efficiency, PMDLs require a shorter analysis time (e.g., with PMDLs, the analysis concluded in 35 min as compared to 250 min with extended domain method) and less computational power (one processor for PMDLs against 20 processors for the extended domain method) thus offering a balance between accuracy and efficiency. Furthermore, illustrative examples of the aforementioned implemented features are presented, namely the response analysis of single-cell and double-cell tunnels exposed to plane waves inclined at an angle.

Problem-independent machine learning-enhanced structural topology optimization of complex design domains based on isoparametric elements

Topology optimization requires dozens or even hundreds of iterations, each requiring a complete finite element analysis (FEA). Significant computation cost limits the application of topology optimization in engineering, especially for high-resolution problems containing complex design domains. To address the issue, a Problem-Independent Machine Learning (PIML) model based on isoparametric elements is proposed. Effectively reducing the computational time of FEA, the proposed model enables efficient topology optimization and extends the solvable problem range to complex design domains. The essential idea is leveraging the substructure method and establishing a mapping from element shapes and material distribution within the substructure to its numerical shape functions through machine learning models. Both sample generation and model training are conducted offline, allowing the trained machine learning model to be directly employed during the topology optimization process. Since the shape function of the substructure is problem-independent, it requires no sample regeneration or modification of the proposed machine learning model when changing the geometry or boundary conditions of the optimization problem. Numerical examples demonstrate that the proposed machine learning model boosts the efficiency of topology optimization by one order of magnitude without parallel techniques.

A multiscale steel–concrete interface model for structural applications

In this paper, a new multiscale macro-element formulation for the steel–concrete interface modeling is proposed. This element allows for the representation of the behavior of steel and the interface zone surrounding it. It can also model the interfacial bond stresses in between. Compared to conventional interface models of the literature, which employ separate mesh elements for the steel and the interface, utilizing a macro-element to model both components simplifies the creation of a reinforced concrete structure mesh. Moreover, the macro-element equilibrium is solved using a sub-structuring method that aims to reduce the computational cost. At the global level, it is considered as a four-node element linked to two-dimensional and three-dimensional concrete elements. At the local level, an assembly of multiple three-node bar elements with bond stresses is performed. An inner mesh discretization is therefore possible at the local level independently of the global level. The coupling between the two modeling scales is done using a static condensation technique. The formulation of the macro-element is presented in this paper. A selection of numerical examples is provided. The presented applications demonstrate the robustness of the proposed interface model and its capacity to reproduce the experimental behavior of reinforced concrete structural elements.

The Possibility of Detrimental Effects on Soil–Structure Interaction in Seismic Design Due to a Shift in System Frequency

Soil–structure interaction (SSI) leads to a modification in the dynamic properties of structure, but due to the complexity of analysis, it is traditionally assumed in seismic designs that the structure is fixed-supported on the ground, which brings about potential risks to the seismic performances of structure. The study works on the possibility of SSI having detrimental effects by comparing the dynamic responses of the SSI system to a fixed-base structure, and presents charts for an evaluation of the system frequency of SSI for the purpose of engineering practice. In order to reveal the physical nature, the SSI model is reduced to its simplest form, consisting of a SDOF oscillator, a three-dimensional rectangular foundation, and a multi-layered half-space. The energy dissipation in the soil is achieved by foundation impedances and the substructure method. Previously, the foundation impedances are usually acquired by two-dimensional or axisymmetric three-dimensional models in uniform half-space to avoid the high cost of the more realistic, fully 3D models, while a high-precision indirect boundary element method is employed, combined with the non-singular Green’s functions of distributed loads to calculate the foundation impedances. Although SSI dampens the peak amplitude of structure response in the frequency domain, case studies on four buildings’ responses to 42 earthquakes in the time history show a possibility of 15–20% that SSI amplifies the dynamic responses of structures, such as the maximum and the mean values in the time history, depending on the properties of the structures and the site, as well as the frequency component of incident waves.

An innovative approach to design readily synthesizable polymers for all-polymer solar cells

The communalization of all-polymer solar cells depends on the cost of active layer materials. We have introduced a framework to find the easily synthesizable polymers. Breaking Retrosynthetically Interesting Chemical Substructures (BRICS) method is used to generate a large database of polymers and synthetic accessibility of generated polymers is predicted. The generated database is visualized using various methods. Energy levels of polymers are also predicted using pretrained machine learning models. Polymers are screened on the basis of predicted properties. Library of polymers are displayed using the T-distributed Stochastic Neighbor Embedding (t-SNE) visualization. Structure Activity Landscape Index (SALI) visualization is also used. A significant change is observed in synthetic accessibility score on structural changes. The histagradient boosting regressor is used to predict the energy levels of polymers that energy levels play significant role in the selection of materials for organic photovoltaic cells. Synthetic accessibility of polymers is analyzed and a significant number of polymers are easy to synthesize. Thirty polymers are selected through screening process that are potential candidates for organic photovoltaic cells.

A Modified Substructure Method for Complex 3D Finite Element Analysis of PVDs with Vacuum Preloading

A Modified Substructure Method for Complex 3D Finite Element Analysis of PVDs with Vacuum Preloading

Site effect on seismic response of buried nuclear power plant structure

The development of multifunctional, multipurpose small modular next generation nuclear reactors has become a trend in designing small-scale nuclear power stations. In this study, taking a new next-generation buried nuclear power structure as the target structure, a 3D finite element model of the site-nuclear power plant structure system was established, and its response at different sites was studied. Using the internal substructure method, this study initially explored the impact of earthquake input and artificial boundary locations on the structural response. The results indicate that the internal substructure method enhances computational efficiency without compromising accuracy. For this model, it is recommended to input earthquake motion close to the structure, set the lateral boundary location to three times the width of the structure, and set the bottom boundary location to two times the depth of the structure. Subsequently, an investigation into the response of the structure at varying site wave velocities is conducted. The results show that as the wave velocity increases, the acceleration response intensifies, while the displacement response diminishes. However, the relationship is nonlinear; the response exhibits less variation with increasing site wave velocity. The seismic response of a structure is profoundly influenced by site conditions, emphasizing the imperative need for careful consideration of specific site characteristics in seismic analyses. In addition, the response pattern of the overall structure is significantly different from that of conventional large surface-sited nuclear power plants, and further research should be conducted on the seismic response characteristics of buried nuclear power plants.

Analysis of Seepage Field Characteristics of Water Diversion Power Generation System of Pumped Storage Power Station

Based on the diversion power generation system project of the Hami pumped storage Hydropower Station, the drainage substructure method is used to accurately simulate the drainage holes of the plant area, and the three-dimensional finite element model of the plant area including the main faults and reflecting the seepage characteristics of complex rock mass is established. Based on the inversion analysis of the natural seepage field, the distribution characteristics of the three-dimensional seepage field in the plant area were studied, and the rationality of the layout of the anti-seepage drainage system was evaluated. The results show that under the action of the anti-seepage drainage system, the groundwater level of the mountain is greatly reduced, and the descending funnel is formed in the three caverns, the seepage flow is controllable overall, and the permeability stability meets the engineering safety requirements.

Mitigating train-induced building vibrations with rubber bearings designed for horizontal earthquake isolation

As rail transportation, including high-speed railways and urban metros, rapidly expands, its proximity to residential areas decreases, raising concerns about ground-borne vibrations. This study investigates the main characteristics of ground-borne vibrations induced by high-speed railways and metros through substructure method based on 2.5D FEM-MFS (finite element method-the method of fundamental solutions). Besides, the study also delves into the vertical vibration behaviors of buildings caused by train operations, evaluating the effect of laminated rubber bearings designed for horizontal seismic isolation on the train-induced building vibration mitigation. Our findings highlight that the broadband vibrations from train operations might coincide with the natural vibration frequencies of buildings, potentially leading to resonance. Implementing isolators allows the natural frequencies of the vibration modes with larger effective masses to be significantly lower than the dominant frequencies of train-induced ground-borne vibration, reducing the high-frequency responses. Additionally, the vibration mitigation enhances with the increase in the thickness of rubber layer. This research not only aims to reduce vibration levels in buildings adjacent to railways, enhancing the living conditions and health of residents but also underscores the engineering innovations necessary for sustainable urban development.

Chemical library generation of polymer acceptors for organic solar cells with higher electron affinity

In this study, an intricate machine learning assisted framework is introduced for the designing of polymer acceptors. Machine learning (ML) models are trained to predict the electron affinity of polymers. Ten thousand new polymers are generated using the Breaking Retrosynthetically Interesting Chemical Substructures (BRICS) method and their electron affinity values are predicted using a pre-trained ML model. The selection of polymers is based on electron affinity, retaining those with higher electron affinity. The synthetic accessibility of the selected polymers is evaluated. Additionally, the structural similarity among the selected polymers is examined; results are indicating higher similarity between selected compounds. By efficiently identifying and optimizing new polymers, the approaches developed significantly enhance the likelihood of discovering superior materials for advanced applications.

Machine learning assisted prediction of band gaps and designing of new polymers for photodetectors: A complete pipeline

The band gap is a crucial parameter of photovoltaic materials, which primarily affects their applicability and performance. Therefore, predicting band gaps of targeted materials accurately and rapidly is of utmost importance for designing photodetectors. In comparison to computationally costly first-principles calculations, machine-learned models can enable accurate, rapid, and low-cost predictions of the bandgaps. In this study, we employ machine-learning tools for the accurate and fast prediction of band gaps of polymers for photodetectors. Importantly, about fifteen (15) regression models are developed and tested to screen the models with highest predictive capability. The best models for targeted predictions among others are the light gradient boosting and hist gradient boosting models. Moreover, similarity analysis is applied to screen/search potential materials for photodetectors with high performance using reference building blocks. Gasteiger atomic charges of reference building blocks are also computed. Harvard organic photovoltaic database is explored to screen new building blocks/ monomers using their Tanimoto index. Both Breaking Retro Synthetically Interesting Chemical Substructures (BRICS) method and human controlled monomer designing methods are used to design monomers using previously searched buildings blocks automatically and manually, respectively. The band gaps of selected organic semiconductors are predicted. In addition, clustering of compounds and heatmaps of selected compounds is constructed. The synthetic accessibility score shows that BRICS-based automatic monomer designing shows negative score (i.e., difficult to synthesize), while human controlled/manual monomer designing shows positive score (i.e., easy to synthesize). Our methodology has immense potential in screening polymers with targeted bandgaps prior to experimental synthesis that could accelerate the designing of new polymers for photodetector and other photovoltaic applications.

Another explicit forms of dynamic substructuring approaches using dynamic constraints at joint nodes

ABSTRACT The dynamic substructuring design describes the dynamic responses in the modal, frequency and physical domains using coupling methods at joint nodes. This paper presents dynamic structuring methods to modify existing substructuring methods by merging the concept of interfacial forces in the satisfaction of compatibility conditions at joint nodes. The algorithms correspond to a type of model reduction approaches that use a few modes of substructures with the assumption of pseudo-masses at the joint nodes of the substructures. It was verified through numerical examples that the pseudo-masses rarely affected the dynamic responses of the synthesized structure. A frequency response function-based substructuring method was derived by applying interface forces at joint nodes to frequency response function (FRF). The proposed dynamic substructuring methods are expressed in explicit forms for synthesizing substructures without any numerical schemes. The validity of the proposed method was illustrated using two numerical examples. The limitations of this study are evaluated using numerical examples.

An efficient modelling approach to obtain dynamic properties of equipment coupled to the bogie of vehicle

Modelling the dynamic interaction between equipment and the bogie of a railway vehicle is crucial for the vibration and fatigue analysis of equipment. Current methods such as finite element and rigid-flexible coupling techniques are cumbersome. In this study, an efficient modelling approach is proposed to obtain the dynamic properties of coupled equipment. First, the vehicle and equipment are treated as independent modules, and further coupled using the frequency response function (FRF)-based substructuring method (FBSM). The FBSM is then developed from the finite element method to derive the FRF for the force to stress of equipment. Validation against experimental data and existing methods for a metro vehicle demonstrates the efficacy of our approach. The results indicate that the proposed approach models the vehicle and equipment as independent modules, eliminating the interdependent modeling steps of the previous approach and requiring less computation. The coupled small-mass equipment has a negligible influence on the dynamic properties of the vehicle bogie, but its dynamic properties are sensitive to vehicle and connection stiffness. Moreover, the vibration fatigue of equipment caused by the wheel/rail roughness can also be analyzed with the proposed approach.

A symmetric substructuring method for analyzing the natural frequencies of conical origami structures

Conical origami structures are characterized by their substantial out-of-plane stiffness and energy-absorption capacity. Previous investigations have commonly focused on the static characteristics of these lightweight structures. However, the efficient analysis of the natural vibrations of these structures is pivotal for designing conical origami structures with programmable stiffness and mass. In this paper, we propose a novel method to analyze the natural vibrations of such structures by combining a symmetric substructuring method (SSM) and a generalized eigenvalue analysis. SSM exploits the inherent symmetry of the structure to decompose it into a finite set of repetitive substructures. In doing so, we reduce the dimensions of matrices and improve computational efficiency by adopting the stiffness and mass matrices of the substructures in the generalized eigenvalue analysis. Finite element simulations of pin-jointed models are used to validate the computational results of the proposed approach. Moreover, the parametric analysis of the structures demonstrates the influences of the number of segments along the circumference and the radius of the cone on the structural mass and natural frequencies of the structures. Furthermore, we present a comparison between six-fold and four-fold conical origami structures and discuss the influence of various geometric parameters on their natural frequencies. This study provides a strategy for efficiently analyzing the natural vibration of symmetric origami structures and has the potential to contribute to the efficient design and customization of origami metastructures with programmable stiffness.