Complex Mechanical Systems Research Articles

Research on decoupled transfer path analysis method and its application in hybrid mechanical systems

As a key technique of vibration control, transfer path analysis (TPA) provides theoretical support for diagnosis, analysis, evaluation, and optimization of vibration in mechanical systems. The coupling phenomena often exists in the vibration transfer paths of complex mechanical systems, which makes most of the existing TPA methods unable to accurately analyze the transfer function of the coupled paths. The classical transfer path analysis (CTPA) is an effective method to solve the problem of path coupling, but it changes the inherent characteristics of the system, the results can not accurately reflect the vibration transfer characteristics of the system. In order to overcome the disadvantages of CTPA method, a decoupled transfer path analysis (DTPA) method is proposed in this paper, and the implementation process of this method is introduced by taking a hybrid mechanical system as an example. The results show that DTPA method can solve the path crosstalk problem and accurately calculate the transfer function of each path.

A new contact and road model for multi-body dynamic simulation of wheeled vehicles on soft-soil terrain

AbstractIn this paper, a new high-performance and memory-efficient contact and road model is developed. Specifically, the road is modeled as a rectangular structured grid of deformable springs in the vertical direction, thus enabling fast execution. The new road model stands out due to its ability to handle large road scenarios by allocating computer memory dynamically for each spring, resulting in efficient memory utilization. Furthermore, each spring represents a small road patch that entails various information, such as the soil elevation, the soil properties, and the soil compaction, allowing for complicated simulations incorporating spatially varying soil properties and phenomena related to the multi-pass effect. In addition, using the new contact model, complex terrain geometries are handled in a computationally efficient way by approximating locally the irregular road profile with a suitable equivalent plane. For this, two different strategies are proposed, namely the radial basis function (RBF) interpolation method and the 3D enveloping contact model. Finally, the proposed techniques are implemented in Altair MotionSolve, a comprehensive multi-body simulation software for complex mechanical systems. In particular, a single-wheel test bed is initially examined followed by a four-wheeled rover model and the next-generation NATO reference mobility model (NG-NRMM). In all cases, the proposed model is validated by using available experimental data. Lastly, a case involving both wheeled and tracked vehicles is also examined by using a shared road model.

Thermal and viscoelastic coupled influences analysis on mixed lubrication in dynamic journal bearings

The interaction of temperature variations and viscoelastic deformation is critical in dynamically loaded journal bearings, especially under fluctuating heavy load conditions. Previous studies have not fully explored the coupling of these two factors. Addressing this gap, this research introduces a novel transient mixed lubrication model that simultaneously considers thermal and viscoelastic effects. This model reveals intricate dynamics influencing the lubrication performance of journal bearings. The findings show that the combined impact of thermal and viscoelastic factors significantly alters the amplitude and phase of lubrication performance parameters. Notably, thermal effects tend to decrease oil film pressure while increasing asperity contact pressure, whereas viscoelastic effects enhance the oil film pressure peak and adjust its phase, concurrently reducing asperity contact pressure. Moreover, varying the eccentric speed profoundly influences the interplay between bearing thermal and viscoelastic effects. This study contributes to a deeper understanding of mixed lubrication in journal bearings and provides technical support for more accurate and predictive reliability improvement in complex mechanical systems.

A New Strategy for Railway Bogie Frame Designing Combining Structural–Topological Optimization and Sensitivity Analysis

Rolling stock manufacturers are finding innovative structural solutions to improve the quality and reliability of railway vehicles components. Structural optimization processes represent an effective strategy for reducing manufacturing costs, resulting in geometries easier to design and produce. In this framework, the present paper proposes a new methodology to design a railway metro bogie frame, combining structural–topological optimization methods and sensitivity analysis. In addition, manufacturing constraints were included to make the component design suitable for production through sand-casting. A robust sensitivity analysis has highlighted the most critical load conditions acting on the bogie frame. Its effectiveness was verified by carrying out two different structural optimizations based on different loadings. Two equivalent designs were obtained. Computational times were positively reduced by about 57%. The maximum value of stress was reduced about 23%. This new methodology has shown encouraging results to streamline the design process of this complex mechanical system, allowing researchers to also include manufacturing requirements.

Mathematical Model Development of an In-Line Robotic Complex Mechanical System

Mathematical Model Development of an In-Line Robotic Complex Mechanical System

Surface plasmon resonance sensor with photodiode integrated beneath plasmonic layer

Surface plasmon resonance (SPR)-based sensors have demonstrated exceptional sensitivity in detecting changes in the refractive index (RI) occurring near the sensor surface due to variations in concentration within the medium, chemical reactions, analyte binding with its ligand, and similar factors. However, most conventional SPR sensors rely on an external photodiode system and a complex mechanical system to measure changes in both intensity and position of reflected light. This is the main limitation of typical SPR sensors, making them non-portable and challenging to apply in experiments beyond the laboratory. Here, we propose SPR sensor chips integrated with a photodiode that eliminates the disadvantage of conventional SPR sensors. The photodiode is based on an n-type Silicon (nSi)-intrinsic Silicon (iSi) junction, sandwiched between a transparent conductive oxide layer, specifically Indium Tin Oxide (ITO), and a plasmonic material, namely Gold (Au). The working principle of our photodiode-integrated SPR sensors was revealed, and the impact of nSi and iSi film thicknesses on sensor sensitivity was investigated. Our SPR sensors enhance the efficiency of SPR-based sensors across diverse applications and facilitate integration into completed electronic devices.

Lagrangian operator inference enhanced with structure-preserving machine learning for nonintrusive model reduction of mechanical systems

Complex mechanical systems often exhibit strongly nonlinear behavior due to the presence of nonlinearities in the energy dissipation mechanisms, material constitutive relationships, or geometric/connectivity mechanics. Numerical modeling of these systems leads to nonlinear full-order models that possess an underlying Lagrangian structure. This work proposes a Lagrangian operator inference method enhanced with structure-preserving machine learning to learn nonlinear reduced-order models (ROMs) of nonlinear mechanical systems. This two-step approach first learns the best-fit linear Lagrangian ROM via Lagrangian operator inference and then presents a structure-preserving machine learning method to learn nonlinearities in the reduced space. The proposed approach can learn a structure-preserving nonlinear ROM purely from data, unlike the existing operator inference approaches that require knowledge about the mathematical form of nonlinear terms. From a machine learning perspective, it accelerates the training of the structure-preserving neural network by providing an informed prior (i.e., the linear Lagrangian ROM structure), and it reduces the computational cost of the network training by operating on the reduced space. The method is first demonstrated on two simulated examples: a conservative nonlinear rod model and a two-dimensional nonlinear membrane with nonlinear internal damping. Finally, the method is demonstrated on an experimental dataset consisting of digital image correlation measurements taken from a lap-joint beam structure from which a predictive model is learned that captures amplitude-dependent frequency and damping characteristics accurately. The numerical results demonstrate that the proposed approach yields generalizable nonlinear ROMs that exhibit bounded energy error, capture the nonlinear characteristics reliably, and provide accurate long-time predictions outside the training data regime.

Unsupervised Bayesian change-point detection approach for reliable prognostics and health management of complex mechanical systems

In the prognostics and health management of mechanical systems, achieving high reliability requires high-quality data for analysis and model training to avoid intensive misalarms and false detections. This research introduces a novel unsupervised Bayesian change-point detection approach specifically tailored to improve the accuracy and dependability of data used in the performance evaluation, fault diagnostics, and predictive maintenance of such systems. By leveraging this method, the precision of change-point detection in operational data is significantly heightened, effectively discerning between normal conditions and potential outliers within the recorded data sequences. The proposed approach replaces the reliance on traditional outlier detection methods with an innovative utilization of prediction error metrics, enriched with statistical analysis to assess and bolster the robustness of the detection algorithm. Conducted case studies on data from the supervisory control and data acquisition (SCADA) system of 92 wind turbines highlight the advanced data cleaning capabilities of our method, which markedly outperforms previous techniques including quartile, Gaussian mixture models, isolation forests, and local outlier factor algorithms. The findings illustrate the methodology’s efficacy in obtaining rcleaned data, which are crucial for the reliable prognostics and health management frameworks of mechanic system, and enhance the confidence in feature extraction and performance prediction.

The Variational Iteration Method for a Pendulum with a Combined Translational and Rotational System

Abstract The dynamic analysis of complex mechanical systems often requires the application of advanced mathematical techniques. In this study, we present a variation iteration-based solution for a pendulum system coupled with a rolling wheel, forming a combined translational and rotational system. Furthermore, the Lagrange multiplier is calculated using the Elzaki transform. The system under investigation consists of a pendulum attached to a wheel that rolls without slipping on a horizontal surface. The coupled motion of the pendulum and the rolling wheel creates a complex system with both translational and rotational degrees of freedom. To solve the governing equations of motion, we employ the variation iteration method, a powerful numerical technique that combines the advantages of both variational principles and iteration schemes. The Lagrange multiplier plays a crucial role in incorporating the constraints of the system into the equations of motion. In this study, we determine the Lagrange multiplier using the Elzaki transform, which provides an effective means to calculate Lagrange multipliers for constrained mechanical systems. The proposed solution technique is applied to analyse the dynamics of a pendulum with a rolling wheel system. The effects of various system parameters, such as the pendulum length, wheel radius and initial conditions, are investigated to understand their influence on the system dynamics. The results demonstrate the effectiveness of the variation iteration method combined with the Elzaki transform in capturing the complex behaviour of a combined translational and rotational system. The proposed approach serves as a valuable tool for analysing and understanding the dynamics of similar mechanical systems encountered in various engineering applications.

Port-Hamiltonian Modelling of Complex Non-Linear Systems, Application to a Liquid Propelled Rocket Engine

Port-Hamiltonian (PH) systems theory is a well-established framework for modelling the dynamics of non-linear systems. It has been used extensively for complex mechanical systems. Recent developments have extended this representation to multiphysical systems. In this paper, a PH formulation of a Liquid Propelled Rocket Engine model is proposed. Starting from a classical state-space representation, a new model is derived which allows to demonstrate the global passivity feature of the system using the property of passivity inheritance by composition of several PH subsystems. The resulting representation allows to consider passivity-based control perspective for this class of complex thermodynamics system.

QUALITATIVE METHODS IN MATHEMATICAL MODELING OF COMPLEX MECHANICAL SYSTEMS WITH DISSIPATION

QUALITATIVE METHODS IN MATHEMATICAL MODELING OF COMPLEX MECHANICAL SYSTEMS WITH DISSIPATION

Calculation of the relative permittivity of Rhodamine 6G using the quantum mechanical method

Background: The article describes a method for calculating the permittivity of organic molecules in quantum mechanics using the well-studied Rhodamine 6G molecule as an example. The study of optical properties of large organic molecules requires not only experimental data but also the use of calculations obtained both analytically and numerically. Objectives: Methods for calculating permittivity as phenomenological characteristics of a sample are to be tested on well-studied molecules to be further applied to more complex nonlinear structures. However, the integral changes need to be approximated in the wave functions of large molecules. Material and methods: The numerical simulations in MATLAB were carried out to be compared with the data from Gaussian 09, which are accurate for such small molecules as Rhodamine 6G. MATLAB calculated permittivity values for the frequency domains corresponding to absorption and fluorescence based on the Fermi golden rule. Hence, any molecule can be represented as a composite quantum mechanical system. Meanwhile, Gaussian 09 used the DFT method to determine permittivity. Results: The Fermi golden rule can be applied due to the representation of the molecule as a complex quantum mechanical system. The proposed numerical methods minimize error by using the Dirac delta function. According to our hypothesis, the sum of the wave function of a particle in a potential well and a particle in a ring equals the wave function of the entire system, thus making it possible to study large molecules. As a result of the calculation for two wavelengths of 337 and 573 nm, the permittivity results calculated using the proposed method in MATLAB are 2.98 and 6.27, respectively. Gaussian 09 calculated the same parameters at 2.85 and 6.23. Conclusion: The resulting datasets show a high degree of correlation. Therefore, the research hypothesis has been confirmed. The selected method also proved efficient, hence the enhancement of luminescence can be achieved by changing the relaxation time of the excited state. Plasmonic nanostructures with predetermined properties will controllably enhance the resulting field by the square of the superposition modulus of their near-field. Consequently, conditions for highly coherent radiation with high intensity and polarization can be predicted and calculated before an experiment is carried out.

Reliability Analysis and Optimization Method of a Mechanical System Based on the Response Surface Method and Sensitivity Analysis Method

Mechanical system reliability analysis constitutes a primary research focus in the field of engineering. This study aims to address the issue of complex mechanical systems with intricate mechanisms and nonlinear reliability equations that are challenging to solve. To this end, we present a reliability analysis and optimization methodology that merges the response surface and sensitivity analysis methods. A comprehensive formation of reliability assessment and optimization of complex mechanical systems is achieved by creating a response surface model to fit the complex state function and solving the reliability parameters, followed by an error sensitivity analysis to determine the mechanical system’s reliability adjustment strategy. Finally, these methods are applied to a cylindrical material transport device to preliminarily realize the reliability assessment and average reliability optimization goals. The study’s findings may offer a theoretical framework and research opportunities to evaluate and enhance the reliability of intricate mechanical systems.

Research on Partial Reduction of Non-Essential Capabilities of Initiating Devices for Complex Mechanical Equipment Systems

Currently, modern spacecraft use high-precision equipment that is sensitive to shock impacts. In this regard, there is a need to create initiating devices with reduced impact. This task determined the vector of development of electromechanical release devices that do not contain pyrotechnic means in their design. Along with new challenges, there are the constant problems of reducing weight, increasing holding force, and reducing response time. The paper presents an analysis of the possibility of upgrading initiating devices in order to improve its characteristics. A team of engineers conducts research using computational and experimental methods. During the work, samples of mechanisms were made and tested to confirm their performance.

Optimisation of System Configuration Using Machine Learning as a Surrogate Model

Complex mechanical systems provide a degree of reliability through redundancy. That is, they have duplicate systems, either similar or dissimilar, performing the same function. These duplicate systems will vary in their dynamic properties and may have different vibration transmission paths due to their connections within the overall system. Therefore, the use of redundancy gives rise to different vibrational behaviour of the overall system depending on the machines selected to deliver the intended system output. In some circumstances, it is necessary to minimise vibration levels by selecting a particular system configuration. In large complex systems consisting of multiple sources, this is not a trivial task, and virtually impossible to investigate all possible machinery combinations leading to the lowest vibration levels, whilst delivering the desired output. To address this, a machine learning model has been trained to provide predictions of machinery vibration levels. Data are obtained using an experimental vibration rig, emulating a complex, multi-source mechanical system. The machine learning model is subsequently utilised within a genetic algorithm optimisation routine in order to obtain the system configuration producing the lowest vibration levels at a number of observer locations for a specified system output.

Hamilton Equations of Pendulum-Spring System

Abstract. The pendulum-spring system was studied by using Hamilton equations of motion. The total Hamiltonian of this system is complicated because of the complex mechanical system. The paper begins by introducing the physical system and defining a fixed coordinate system. Following this, the Lagrangian and Hamilton equations of motion are derived. Six equations of motion are obtained from the Hamilton equations in the form of ordinary differential equation. These equations can be employed for simulating the dynamical behavior of the system. The primary goal of this paper is to familiarize physics students with the Hamilton equations of motion as applied to the pendulum-spring system.Keywords: Hamiltonian, pendulum, equation of motion, ODE

Vibration Signal Analysis of Complex Mechanical Systems and Early Wear Detection and Forecasting for Gears

Vibration Signal Analysis of Complex Mechanical Systems and Early Wear Detection and Forecasting for Gears

Influence of clearance between cradle and barrel on nonlinear dynamic response of the artillery system

The artillery weapon system,characterized as a complex nonlinear mechanical system, is influenced by various factors affecting its muzzle vibration response.In the article, the model of the artillery system with variable cross-section and multistep barrel is established on the basis of the bending deformation equation, and the fundamental frequency equation is obtained, and the auxiliary verification is completed through modal testing and finite element methods. Then, considering the clearance between the barrel and the cradle, the equivalent nonlinear dynamic model of the vertical vibration of the muzzle is established is established. Furthermore, the multi-scale analysis method obtains the amplitude-frequency curve of the equivalent model under sinusoidal excitation, and analyzes the influence of parameters on the response characteristics. Finally, the vibration research system of the model of the artillery system is constructed to study the vibration characteristics of the muzzle. The results show that the equivalent nonlinear dynamic model established in the paper has certain accuracy, which can provide a certain reference for the related research of the artillery system

Role of secondary components in the numerical analysis and in-plane seismic performance assessment of glass curtain walls

Glass façades are complex mechanical systems, in which brittle and vulnerable glass panels interact with metal members and secondary components. Under extreme design actions, such as seismic events, glass failure in tension (cracking) or compression (crushing) is a critical condition for structural performance assessment. Compared to full-scale experiments, in this regard, Finite Element (FE) numerical tools can offer a robust support in design. Besides, many primary and secondary façade components should be properly taken into account, because responsible of possible major approximations in their expected mechanical interactions. In this paper, the in-plane seismic response of glass curtain walls is investigated with geometrically accurate and detailed (“MREF”) or simplified and efficient (“MSIMP”) numerical models. Comparative results are critically discussed, based on dynamic numerical simulations, with a primary attention which is focused on the mechanical performance of glass panels.

Simulation-driven Bearing Fault Diagnosis for Condition Monitoring without Faulty Data

The failure of rolling element bearings in complex mechanical systems is a significant cause of mechanical failures, leading to decreased productivity and safety risks. Deep learning has shown promising results in bearing fault diagnosis, but the predictive performance depends on highquality data. Domain adaptation has been studied to solve this problem, but it still has limitations when applied to real-world industrial applications. In this study, we propose a deep learning-based domain generalization framework for bearing fault diagnosis using the bearing simulation model and adversarial data augmentation method. The proposed framework was validated on a real bearing fault dataset and showed promising results in improving diagnostic performance in cases where fault data cannot be obtained or when dealing with unlearned target domains. This approach has the potential to improve industrial maintenance systems by obtaining improved generalization performance in the absence of fault datasets.