System Structure Model Research Articles

Structural System and Computational Dynamic Model of a Life-Saving Multi-Storey Building with a Kinematic Seismic Isolation System

Introduction. In design of a life-saving multi-storey building, a seismic isolation system in the form of the kinematic supports is used to ensure the seismic resistance and reduce the seismic loads. The structural system, the computational dynamic model and the results of the intermediate in-situ tests of a life-saving multi-storey building with a kinematic seismic isolation system being built in Grozny have been analysed in the article.Materials and Methods. The research included modeling and computation of the structural system of a building with a kinematic seismic isolation system. In-situ tests were carried out to confirm the working capacity of the seismic isolation system connections.Results. A multi-storey life-saving building was designed according to a frame-core wall structure, where the vertical load-bearing elements were core walls, columns and connections between the columns in the form of the stiffening diaphragms. The high-rise part of a building was designed using a kinematic seismic isolation system consisting of the seismic isolating concrete-filled steel tubular supports. The results of the calculations of the main and specific load combinations and internal forces in the load-bearing structures have been presented.Discussion and Conclusion. The research has shown that the use of the developed structural system of seismic isolation with kinematic supports makes it possible to reduce the seismic loads and the total weight of a building and at the same time to increase its mechanical reliability and safety. The conducted intermediate in-situ tests have confirmed the working capacity of the joints connecting the supports with the monolithic reinforced concrete structures of a building, which makes it possible to implement the kinematic seismic isolation supports into the construction industry practices.

A sandwich-type Winkler-Pasternak double elastic foundation beam model for analysis of nut column load transfer systems of shiplifts

The rack and pinion shiflifts feature its capability of fortification against extreme accidents of unbalance of ship chamber. This requires the load transfer systems of nut columns to be able to transfer the huge and centralized loads from the safety mechanisms to the tower columns in a distributive bearing way. Aiming at exploring a design-oriented computation method for the nut column load transfer systems (NCLTSs), a sandwich-type semi-infinite coupling Winkler-Pasternak double elastic foundation beam model (DEFBM) has been established, with a distributive assumption of the axial constrain force on the interfaces between the nut columns, mortar beds, adjusting beams and the second stage concrete based on the physical model test, has been proposed. A set of formulars for describing the distribution of deflections, internal forces and stress of nut columns and adjusting beams have been derived. A case study of the Three-Gorges shiplift has been conducted to demonstrate the approach of calculation of internal forces and stress by applying the formulas based on sandwich-type Winkler-Pasternak DEFBM, showing the convenience and rationality of the method to parametric layout design of the nut columns and adjusting beams in NCLTSs. Not only to NCLTSs and rack load transfer systems of shiplifts, the sandwich-type Winkler-Pasternak DEFBM also supplies the reference to the modeling and analysis of the rail-embedment structural systems broadly applied in hydraulic engineering.

A Review of Structural Systems to be Built on Planets

The architectural construction process on planets is an architectural issue that develops day by day due to extreme environmental conditions and uncertainties. Architectural design needs structural systems to survive. Structural systems on planets encounter load factors that are different from the load factors on Earth. Choosing the optimum structural system is important for the structures planned to be built on planets to survive under the effects and loads of the environment and to adapt to human physiology. Some of the different types of structural systems used on Earth are featured in the literature for building a structure on planets. An evaluation system has been created to determine the correct system type for the first settlements on planets among the prominent structural system types and to narrow down the selection area of these system features. In line with this evaluation system in this study, a structural system model that stands up to harsh environmental conditions and protects human health is proposed for the first settlements on the planets. It is aimed that the evaluation system will be developed in the light of research emerging from developing technologies and can be used in future studies.

An Innovation Management Approach for Electric Vertical Take-Off and Landing

With more companies entering the realm of electric vertical take-off and landing (eVTOL) and governments enacting policies to support the development of low-altitude economies, the commercial potential of eVTOL is being recognized by the public. However, true commercialization is still a long way off. This article analyzes the technologies, product features, potential markets, and government policies related to eVTOL and constructs a four-stage, four-layer Policy–Technology Roadmap (P-TRM) model to guide the R&D process of eVTOL. Then, it is transformed into a system structural model, and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used to identify several key nodes in the R&D process. Utilizing the Technology Adoption Life Cycle (TALC) theory for interpretation and analysis, the article concludes by proposing strategies in product, technology, and policy support for how eVTOL can successfully cross the chasm. This preliminary analysis of the development path, key nodes, and necessary measures for crossing the chasm provides insights for the R&D and commercialization of eVTOL.

Hierarchical Bayesian modeling for calibration and uncertainty quantification of constitutive material models: Application to a uniaxial steel material model

Constitutive material models play an integral role in finite element (FE) modeling of structural components and systems and are governed by a set of parameters. The values of the material model parameters are important for the FE model to be able to predict the real structural behavior as closely as possible. Realistic cyclic material constitutive models well calibrated to experimental data are critical when predicting the nonlinear dynamic response of structural systems to extreme loads (e.g., earthquakes, high winds, blast). Hence, this study focuses on the inference and uncertainty quantification of material model parameters from an experimental dataset consisting of data from experiments on multiple specimens of the same kind, which is a topic of great interest to the structural modeling and simulation community. Traditionally, Bayesian inference is performed by pooling the data from all the specimens, or by considering the data from a single specimen, disregarding the inherent variability (aleatory uncertainty) among specimens of the same kind. Consequently, traditional approaches fail to capture the population distribution of material model parameters, which represents the aleatory specimen-to-specimen variability that is essential for conducting reliability analysis and uncertainty propagation studies. Moreover, these approaches cannot accurately simulate the response of new specimens. To overcome these limitations, this study introduces a novel application of hierarchical Bayesian inference for calibrating material model parameters. By simultaneously considering experimental data from multiple specimens of the same kind, hierarchical Bayesian modeling quantifies both epistemic uncertainty and aleatory specimen-to-specimen variability. In this study, the parameters of the well-known Giuffré-Menegotto-Pinto (GMP) uniaxial material model for reinforcing steel are calibrated and validated based on a dataset of experimental cyclic tests conducted on thirty-six steel specimens, or coupons, originating from three different manufacturing mills, complying to two different manufacturing standards, and subjected to different strain histories. These strain histories are similar to those experienced during seismic events by reinforcing steel bars within reinforced concrete structures. The material model parameters are calibrated using both the traditional and the hierarchical Bayesian approach and the results are compared. Based on the experimental dataset employed in this study, a joint probability distribution of the GMP material model parameters that quantifies both epistemic (due to finite sample size) and aleatory (coupon-to-coupon) variability or uncertainty is provided which can be used for uncertainty propagation in reliability and risk analyses of nonlinear dynamic systems.

Perception on Tax System Structure, Tax Compliance Costs and Tax Compliance Behaviour in Yemen

This study considered the tax system structure into tax penalty, tax rate, and tax audit, and then examined their effects on tax compliance behaviour among small and mediumsized enterprises (SMEs) in the manufacturing sector of Yemen. Also, the tax compliance cost in Yemen was considered as a mediating factor in the tax system structure and tax compliance model. The study used a survey questionnaire to collect data based on previous studies. The study empirically found a strong positive and significant association between the tax rate, tax penalty, and tax audit and manufacturing SME tax compliance behaviour in Yemen. Whereas tax compliance cost is negatively related to tax compliance behaviour. For the mediation effect, only the relationship between two factors, that is tax rate and tax penalty with tax compliance behaviour were mediated by of tax compliance costs. While no evidence for the mediating effect of tax compliance costs on the relationship between tax audit and the tax compliance behaviour is found. By referring to Deterrence Theory, this study has contributed by extending the tax literature through a framework that examined tax compliance cost as a mediator in the association between manufacturing SME tax compliance behavior in Yemen and the structure of the Yemeni tax system.

Demand Response Power-Gas Interconnection Energy System and Power Metering Based on SOGA

Under the double background of global energy crisis and environmental pollution, China vigorously develops renewable energy while accelerating the construction of energy Internet. Taking demand response into account, this paper proposes the optimal design of power-gas interconnection energy system and power metering, and establishes the optimization research model of IEGES (integrated electricity-gas energy system) with demand response. Firstly, the structure model of the electric-gas interconnection energy system with CHP (Cogeneration, combined heat and power) as the core is constructed, and the energy conversion relationship and different energy flow directions of the coupling equipment are expounded from three aspects. The natural gas source point, pipeline equation, power side branch equation, voltage and current equation are modeled and sorted out, and the square term in the equation is linearized by second-order cone programming method, and the mixed integer nonlinear programming problem is transformed into mixed integer linear programming problem. A single objective genetic algorithm with “elite strategy” was selected to solve the equipment capacity optimization problem of IEGES system with system economy as the optimization objective. After a long time of parameter combination attempts, the current population size is 30, the number of iterations is 600, the crossover rate is 0.8, and the heritability is 0.3. The above parameters can obtain better convergence results on the basis of considering the operation time. Finally, a stochastic optimization method of energy Internet considering integrated demand response and uncertainty of wind power is proposed, which aims to meet the energy demand of end users while minimizing the operating cost of the system. The comprehensive demand response strategy including internal and external demand response is considered in the model. Internal demand response is realized by adjusting the internal operation mode of EH, while external demand response is implemented by the end user’s active response, and the load is time-shifted or interrupted under the guidance of external signals.

Deep learning-based digital twin for intelligent predictive maintenance of rapier loom

The rapier loom works in a complex environment and operates at high speeds. It is inevitable that its performance will deteriorate during the production process, which in turn will cause faults. The development of maintenance has undergone the transition from “regular maintenance” and “post-event maintenance” to “predictive maintenance”. In order to achieve the synergistic optimization goal of ensuring operational safety and reducing operational costs, a predictive maintenance method driven by the fusion of digital twin and deep learning is proposed based on the idea of “combining the real with the virtual and controlling the real”. Firstly, a digital twin system structure model of rapier weaving machine is constructed, and the overall architecture of digital twin is proposed according to the full operation cycle of rapier weaving machine. Then, the digital twin-driven process parameter evaluation and prediction and health state evaluation and prediction are investigated separately. In order to achieve the evaluation and prediction of process parameters to ensure the efficiency of weaving machine operation, the prediction method of IWOA optimized BP neural network driven by twin data is proposed and the model is updated and optimized based on the martingale distance approach. In order to achieve health state assessment and prediction, we use health index as an evaluation index to characterize the health condition of spindles, and use BiLSTM network to achieve prediction of remaining spindle life and then make maintenance decisions. The results show that there are greater advantages to combining deep learning and digital twin technology for intelligent predictive maintenance of rapier loom.

Insertion Performance Study of an Inductive Weft Insertion System for Wide Weaving Machines

Wide weaving machines traditionally enhance the weaving width by increasing the shuttle’s initial velocity. However, this approach introduces challenges like pronounced equipment vibration, elevated noise levels, heightened energy consumption, and a reduced lifespan. Moreover, its efficacy in significantly widening fabric is constrained. Addressing these concerns, this paper proposes a wide-width warp insertion solution that involves driving the high-temperature superconducting shuttle to achieve high-speed horizontal flight through a traveling magnetic field. The inductive weft insertion system structure of wide weaving machines comprises an insertion guideway with an iron core and wound electromagnetic coils. The shuttle consists of a high-temperature superconducting block and a conductive plate, serving as the driving element. By establishing the equivalent circuit of the weft insertion guideway and the suspended shuttle, the calculation formula for the dynamic driving performance of the weft insertion guideway is obtained. Utilizing a transient 3D magnetic field simulation model, the impact of parameters like the current frequency, shuttle conductive plate thickness, and suspension gap on weft insertion performance is explored. Successful wide-width weft insertion motion is achieved by controlling coil input current parameters. Finally, an experimental platform is constructed to validate the correctness of the weft insertion system structure and simulation model through practical experiments.

Reactive power capacity allocation technology of shore‐based power supply system of offshore oil production platform under different network topology reliability measures

AbstractTo improve the economy of offshore oil production platform shore‐based power transmission mode, the reactive power capacity allocation technology of offshore oil production platform shore‐based power supply system based on different network topology reliability measures was proposed. The optimization model of power supply system network topology and the structure model of voltage source converter high voltage direct current transmission system were established, the start and stop control scheme of flexible direct current (DC) transmission system was designed, and the operation parameters of converter station and DC line were monitored in real time. The topological structure of offshore oil production platform shore‐based power supply system is constructed, the mathematical model of flexible DC transmission system is established when the alternating current side voltage is balanced, the reactive power capacity configuration of offshore oil production platform shore‐based power supply system is optimized, and the life cycle cost results are calculated. The experimental results show that the algorithm proposed in this paper can obtain the optimal solution only after 26 calculations, with high optimization efficiency and good convergence effect. Reactive capacity configuration of shore‐based power supply system for offshore production platform can reduce failure rate and life cycle cost.

Structural system modeling from base excitation measurements using Swarm Intelligence

The problem of system identification is classified as an inverse problem, and it concerns the derivation of mathematical models from experimental data. Problems of this kind can be addressed using optimization techniques. This paper utilizes a hybrid form of the well-known Particle Swarm Algorithm (hPSO) to produce a mathematical model representative of the dynamic behaviour of a generic structure subjected to a base excitation, directly from acceleration measurements. The proposed algorithm is specifically tailored for this application; physical properties of the matrices are assured introducing a 'healing' process. Problem-specific operators from Evolutionary Strategies are used to improve the solution and avoid a local minimum. The final adjustment of the solution is delegated to a local optimization algorithm.

Research on the Construction of Indicator System for the Quality of Kindergarten Teacher’s Work Environment

The quality of the working environment for kindergarten teachers not only has a crucial impact on their work engagement and job happiness, but also has a crucial impact on the quality of kindergarten education. At present, there is a lack of evaluation tools for the quality of the working environment of kindergarten teachers in China, which to some extent limits the current investigation and research on the quality of the working environment of kindergarten teachers. Therefore, this article adopts text analysis, questionnaire survey, and interview methods to form a project library based on existing relevant teacher work environment questionnaires, domestic and foreign policy documents, and teacher interviews. Combining organizational climate theory, data is collected through three rounds of questionnaire surveys, and exploratory factor analysis and confirmatory factor analysis are used to construct a quality indicator system for kindergarten teacher work environment. The indicator system structure model constructed by the research institute has a good fitting degree, good reliability and validity, and can be used to measure the quality of the working environment of kindergarten teachers. It has a positive significance for caring for the working environment of kindergarten teachers and dynamically adjusting preschool education resources at the practical level.

Development of modular and reusable AI models for fast predicting fire behaviour of steel columns in structural systems

It is important to model the local failure of structural members as well as the global responses of a structural system when it is exposed to fire. The local failure of columns such as buckling requires high-resolution models, which could be not used for modelling a structural system for its computational cost. Inspired by the hybrid simulation technique, this paper aims to develop modular AI models as a first attempt to predict the local performance of a specific steel column exposed to fires and to enable the future communication with structural system model. Two kinds of machine learning methods namely artificial neural network (ANN) and support vector regression (SVR) are utilized to train AI models based on a large numerical dataset extended from a 3D detailed shell model of columns after validation against fire test results. The excellent prediction performance of AI models is shown regarding the mean squared error (MSE), mean absolute error (MAE), coefficient of the variation of the root mean square error (cvRMSE), variance accounting for (VAL) and coefficient of determination (R2). Moreover, the capability of AI models is demonstrated in predicting fire behaviour of columns in untrained steady heating and transient heating scenarios. A further demonstration is performed within a plane-frame structure simulated with a hybrid-scale model and considering standard fire heating on columns, which shows great potential of the AI models towards further implementation in hybrid simulation.

Structure Improvement and Optimization of Gantry Milling System for Complex Boring and Milling Machining Center

To enhance the efficiency and machining precision of the TX1600G complex boring and milling machining center, a study was conducted on the structure of its gantry milling system. This study aimed to mitigate the influence of factors such as structural quality, natural frequency, and stiffness. The approach employed for this investigation involved mechanism topology optimization. To initiate this process, a finite element model of the gantry milling system structure was established. Subsequently, an objective function, comprising strain energy and modal eigenvalues, was synthesized. This objective function was optimized through multi-objective topology optimization, taking into account certain mass fraction constraints and considering various factors, including processing technology. The ultimate goal of this optimization was to create a gantry milling structure that exhibited high levels of dynamic and static stiffness, a superior natural frequency, and reduced mass. To validate the effectiveness of these topology optimization results, a comparison was made between the new and previous structures. The findings of this study serve as a valuable reference for optimizing the structure of other components within the machining center.

Analysis of the environmental resources value and enterprises economic benefits based on combination algorithm

Abstract To solve the problems of fuzziness, nonlinearity and complexity of economic factors and environmental factors in the evaluation of ecological economic benefits of enterprises are studied. Based on the theory of environmental-economic benefits (EEB), this paper defines the value of environmental resources and economic benefits of enterprises, and puts forward the indicators of environmental and economic benefits. The evaluation model of enterprise EEB based on the analytic hierarchy process and fuzzy set algorithm is constructed. By establishing the hierarchical structure model of index system, the intuitive fuzzy judgement matrix is obtained, and the final score of enterprise environmental benefits is calculated by consistency test and weight analysis. After verifying the model, the evaluation method of enterprise EEB proposed in this paper is accurate and reliable, which is helpful to deeply analyse the hidden order and adaptation mechanism of complex adaptation system of enterprise environmental management.

A conditionally constrained compound sub‐gradient method for distributed energy coordination

AbstractA conditionally constrained compound (CCC) sub‐gradient method typically involves solutions by multi‐agents, which have important applications in distributed energy resources (DER). The proposed DER system structure model uses operating state switching to analyze the characteristics of the model transformation. The work proposes that energy storage (ES) transformation is the main factor that influences the transformation in the DER model. For solving this optimization problem, this study uses a compound sub‐gradient method that is distributed among the agents. Compared with the conventional analysis methods, a system configuration and equivalent model of distributed power is obtained using the sub‐gradient method, and the power distribution characteristics of the DER in the off‐grid state are further given. Using a time‐based representation, this method can be applied to complex distributed new energy access applications and uses an iterative compound step ladder algorithm with a specific step size for switching. The compound distributed gradient algorithm further determines the selected range of algorithm parameters. Finally, the results of the convergence rate explicitly characterize the effectiveness of the method.

Retracted: Analysis on Organizational Structure System Model of China’s Sports Management System Based on KNN Algorithm

Retracted: Analysis on Organizational Structure System Model of China’s Sports Management System Based on KNN Algorithm

Research on Intelligent Design Method and Technology of Grenade Driven by Digital Twin

In order to change the design method of grenade based on hand drawing and traditional CAD numerical calculation, solve the problem of long design cycle caused by repeated design because the result of grenade design does not meet the tactical index, and promote the transformation of grenade design to the direction of intelligence, based on the concept and architecture of digital twin, a five-layer structure model of the intelligent design system of grenade driven by digital twin is established, and the relationship and connotation of each layer are analyzed: the preliminary realization scheme of applying digital twin technology and intelligent design technology to grenade design is discussed, which provides theoretical reference for subsequent practical application.

Mapping and change information between structural and behavioural domain – Behaviour prediction framework

During product development it is very important to do research in comparing the system functionality throughout its working operation and when unexpected parameters occur. In modelling of the system behaviour, the uncertain effects of the system due to the influence of the changing working environment was also considered here. The observed system was presented with its two models: structure model and behavioural model. The system structure model was represented in matrix notation with a Design Structure Matrix (DSM). The second domain of the system, i.e. its behaviour, was modelled by the Model Predictive Control (MPC) method and by studying the system stability using the second Lyapunov method. The mutual transfer of information between these two domains allows the creation of system variants suitable for working in uncertain situations. By combining the developed environment (Behaviour Prediction Framework) and its algorithm, and using existing computer tools (©LOOMEO and ©MATLAB), the research results were verified. This was done with a case study of an air handling unit used for air treatment in the food industry and clean rooms. While carrying out the simulation of the behaviour of the air conditioning system, the following facts about influential parameters were established. The most influential parameter for the systems stability was the environment temperature from −40 to −25 °C. The reason was that heat transfer coefficient was then very large. In addition, high air flow rate in range from 3.6 to 7.8 m3/s, was had also a great influence on stability. System instability was happened with smaller geometries (the cross-sectional area – height ×weight) of the air handling unit.

EXPERIMENTAL AND THEORETICAL STUDIES OF DYNAMIC CHARACTERISTICS OF WOODEN FRAME BUILDINGS

The research carried out in this paper is reduced to studying the design features of wooden frame structures and analyzing the nature and degree of influence of individual structural elements on the work of the frame as a whole based on a comparison of experimental and theoretical data. The results of the studies with the evaluation of natural oscillations are the basis for the correct modeling of structural systems when performing static and dynamic calculations.