Mechanical Systems Research Articles

Multiple lubricated joints with long and short bearings in multibody mechanical systems - Modeling, simulation, and performance analysis

This study examines the effect of multiple lubricated imperfect long and short bearings on the performance of a multibody mechanical system. Unlike the typical assumption of a single perfect or imperfect lubricated joint due to modeling difficulties, this research considers the practical impacts of clearances in multiple joints. While unlubricated joints generally cause significant performance issues, lubricants reduce these effects, creating more localized peaks. The findings show that as the number of lubricated joints increases, both the magnitude and frequency of these peaks rise. In systems with multiple journal bearings, torque peaks become more noticeable due to the additional degrees of freedom introduced by the clearances. These degrees of freedom amplify acceleration, leading to higher lubricant reaction forces, which in turn require greater motor torque peaks to maintain the system's kinematics. Shorter lubricated joints exhibit more severe peaks than longer ones, mainly due to side leakage causing axial pressure variation and reduced damping capacity. The study highlights the need to replace idealized joints with imperfect ones for more accurate modeling of practical systems.

Recent Advances in Biomimetic Related Lubrication

Friction is ubiquitous in industry and daily life, which not only leads to the wear and tear of equipment and machinery, but also causes a lot of energy waste. Friction is one of the significant factors leading to energy loss in mechanical systems. Therefore, it is essential to minimize friction losses. Creatures in nature have evolved various surfaces with different tribological characteristics to adapt to the environment. By studying, understanding, and summarizing the friction and lubrication regulation phenomena of typical surfaces in nature, various bionic friction regulation theories and methods are obtained to guide the development of new lubrication materials and lubrication systems. This article primarily discusses the study of lubrication mechanisms through biomimetic design, which is mainly divided into chemical approaches, structural strategies, and chemical–structural coupling approaches. From the chemical point of view, this paper mainly summarizes joint lubrication and engineering lubrication in biomedicine, with inspiration from lotus leaves, fish skin, and snake skin, each with unique antifriction structures which are famous for their super hydrophobicity in nature. Finally, chemical–structural coupling simulates the lubrication mechanism of natural organisms from the joint action of biological structures and chemical substances, and is applied to coating design, so as to reduce the friction and wear on coating surfaces, improve the durability and anti-pollution ability of coatings, significantly improve the tribological performance of mechanical systems, promote scientific innovation, and promote energy conservation, emission reduction, and sustainable development.

Artificial intelligence and machine learning for additive manufacturing composites toward enriching Metaverse technology

<p class="Abstract">As a result of the growing significance and application of technology across a wide range of fields, digital environments such as Metaverse started to take shape over the span of the previous decade. This study aims to discover an area of engineering that could benefit from this new technology by developing an artificial intelligence (AI)—based approach to analyzing and predicting the mechanical properties of carbon fiber reinforced syntactic thermoset composites that are made through additive manufacturing (AM). These composites are intended to be utilized as a tool for metaverse technology in a variety of domains—as the presence of the limitations in the currently experimental methods. The metaverse allows for the generation of simulations through the application of artificial intelligence (AI) and machine learning (ML). Consequently, this paves the way for individuals to investigate various design possibilities and view the virtual manifestation of those possibilities. This is made possible by the use of machine learning algorithms, which allow for the monitoring and evaluation of user performance, as well as the provision of individualized feedback and suggestions for improvement. As a consequence of this, it is feasible that professionals will be able to get education and training that are both more efficient and effective. Consequently, this work aims to introduce an Adaptive Neuro-Fuzzy Inference System (ANFIS)—based model, which is able to effectively anticipate the behavior of mechanical systems in a variety of settings without the need for significant measurements. The validity of the ANFIS model was determined through the utilization of flexure and compression testing. The approach that was used to improve the technical assessment of the manufactured composites—is verified by the model’s near-realistic predictions. Moreover, this method is superb for lowering weight, enhancing mechanical qualities, and minimizing product complexity.</p>

Open-loop swept frequency response of nonlinear structures subjected to weak coupling

AbstractThe present study demonstrates a common behaviour of a forced nonlinear structure with smooth nonlinearity, while coupled dynamics are apparent, originating from the attached electrodynamic shaker. This appears as a variation in the transmitted forcing amplitude and is often subjected to a hysteretic (multi-state) behaviour for up and down open-loop sweeping. This situation differs from the ideal constant amplitude harmonic excitation, on which parameter extraction and engineering comprehension are based on. Untreated or ignored, this can lead to the misinterpretation of the underlying dynamics through the measured nonlinear frequency response curves and their force-normalised version, often called quasi-frequency response function. In this paper, a post-processing solution is introduced for the correct interpretation of frequency response curves at constant forcing amplitudes through the open-loop construction and resectioning of the so-called frequency response surface. The phenomenon and the proposed methodology are demonstrated using a two-degrees-of-freedom model on a shaker-nonlinear beam structure. First, open-loop frequency sweeps are executed on the mechanical system to create the nonlinear frequency response surface, where their actual amplitudes and hysteresis widths are significantly different from the ideal constant forcing amplitude case. The response surface is then sectioned at the assumed constant forcing values by using an appropriate interpolation law. These resectioned curves represent the forced nonlinear standalone structure under ideal constant harmonic excitation. The frequency response surfaces are characterised and resectioned on a nonlinear structure with stiffening and softening cases. Furthermore, an improvement in the operational resonance decay (ORD) method in its filtering and automation is shown to extract the backbone curves (BBCs). The BBC and the resectioned surface provide a complete picture and cross-validation of the underlying dynamics. Finally, the BBC and its distortion are also shown in the response surfaces in relation with the excitation normalization.

A novel demodulator design for signal processing of inductive oil debris sensors

Abstract Metal debris from bearing wear occurred in the lubrication system of aircraft engines represents valuable information, which can be utilized to assess the current engine condition and further enable the life-cycle prediction. The demodulation of debris signals consists of two parts: mixing and filtering. In traditional methods, the mixing process is complex to adjust and prone to failure, while the filtering employs the fixed low-pass filtering, which significantly affects the demodulation performance when the debris flow rate changes. To address these issues, this paper proposes a novel demodulation model to enhance the demodulation performance and increase the signal-to-noise ratio of chip signals. Firstly, the circuit innovatively adopts a self-frequency mixing method to avoid the complex phase adjustment required by traditional reference-frequency mixing methods for the improved stability of the circuit. Following that, a digital potentiometer is employed to implement the flow rate filtering, enabling precise control over the filter type and range, thus mitigating signal distortion caused by changes in flow rate and increasing the signal-to-noise ratio. Throughout extensive experiments and data analysis, the self-frequency mixing method improves the signal-to-noise ratio by 0.72 dB and reduces the signal standard deviation by 59.12% as compared to the reference-frequency mixing method. Considering the debris flow rate in the range of 0.2 to 0.5 m/s, the flow rate filtering method improves the signal-to-noise ratio by 19.71 dB and reduces the amplitude standard deviation by 66.18%, as compared to results by the traditional fixed low-pass filtering method. Results demonstrate that the aforementioned circuit design effectively enhances the signal amplitude and stability of inductive oil debris sensors in the wear assessment of mechanical systems and provides useful insights into the development of advanced sensors used in the field of structural health monitoring.

Static and dynamic aspects of the principle of virtual work

AbstractThe paper presents a detailed analysis of the principle of virtual work in its statics and dynamics aspects. Special attention is paid to not exact formulation and interpretation of mechanical system motion equations based upon this principle, what is presented quite often in text books. The general form of the principle of virtual work and variational formulations of the problems in statics are presented and analyzed. Additionally, the dynamic implications of the principle of virtual work and dynamics problem formulation of a system subjected to scleronomic constraints are detailed. The basis for the formulation is the dynamic implication of the principle of virtual work that specifies the relation between the constraint reactions and the body accelerations. This relation takes the form of the variation inequality. As the result, the Gauss principle which determines the body acceleration vector is presented. Furthermore, a description of impact formulation, based upon the Carnot model, caused by the constraints is provided. The theoretical development is illustrated with examples of applications of the principle of virtual work.

Pedagogical moves related to analogy that support a unified understanding of eigentheory concepts in a quantum mechanics class

It is beneficial for quantum mechanics students to have a unified understanding of eigentheory concepts, so they can recognize the shared structure of mathematized phenomena from the different quantum mechanical systems of spin, energy, or position and recognize those as instantiations of the same overarching concept. Quantum mechanics instructors should, therefore, provide opportunities for their class community to develop a shared unified understanding of eigentheory concepts. One such opportunity can arise by engaging students in analogizing eigentheory concepts in one context with those from another context. We investigate the pedagogical moves related to analogies that can be used by a quantum mechanics course instructor to support a class community in developing a shared unified understanding of eigenequations. We analyze classroom data to characterize an instructor’s pedagogical moves as he engaged students in analogical reasoning. Some moves include posing tasks conducive to analogizing; preparing, soliciting, and scaffolding students’ participation in analogical reasoning; using deictic gestures and inscriptions; juxtaposing symbols representing the analogized concepts; and explicitly highlighting the sameness of the analogized concepts. We exemplify these pedagogical moves with analytical descriptions of illustrative class episodes. We discuss how these pedagogical moves can support the class community’s expansion of their common ground by fostering the development of the class’s shared unified understanding of eigentheory concepts. Published by the American Physical Society 2024

Multi-Objective Optimization of a Small-Scale ORC-VCC System Using Low-GWP Refrigerants

The increasing global demand for energy-efficient cooling systems, combined with the need to reduce greenhouse gas emissions, has led to growing interest in using low-GWP (global warming potential) refrigerants. This study conducts a multi-objective optimization of a small-scale organic Rankine cycle–vapor compression cycle (ORC-VCC) system, utilizing refrigerants R1233zd, R1244yd, and R1336mzz, both individually and in combination within ORC and VCC systems. The optimization was performed for nine distinct cases, with the goals of maximizing the coefficient of performance (COP), maximizing cooling power, and minimizing the pressure ratio in the compressor to enhance efficiency, cooling capacity, and mechanical reliability. The optimization employed the Non-dominated Sorting Genetic Algorithm III (NSGA-III), a robust multi-objective optimization technique that is well-suited for exploring complex, non-linear solution spaces. This approach effectively navigated trade-offs between competing objectives and identified optimal system configurations. Using this multi-objective approach, the system achieved a COP of 0.57, a pressure ratio around 3, and a cooling capacity exceeding 33 kW under the specified boundary conditions, leading to improved mechanical reliability, system simplicity, and longevity. Additionally, the system was optimized for operation with a cooling water temperature of 25 °C, reflecting realistic conditions for contemporary cooling applications.

Passive House Design: A Possible Energy Efficient Option in the Building Sector of Nepal

The diversified geography of Nepal creates huge variations in the country's climatic zones; however, the building industry has so far used standardized methods that tend to neglect local climate conditions. Most of these standards then rely on energy-intensive mechanical systems to maintain indoor thermal comfort, without considering more viable and climate-responsive design methods. In this light, the development of a climate classification related to building design will help develop and encourage energy- and climate-effective building architecture in Nepal. The existing energy-saving practice in the building sector of the country is reviewed in this paper, and it outlines ways to improve the adaptation of energy-efficient methodology. It shows that passive houses are performing much better in comparison with modern constructions that are behind in terms of energy efficiency when compared with traditional homes. The study outlines the climate-specific design criteria and methodologies for various regions and sets the path for exploration of the passive house design process challenges and opportunities that might exist for wider diffusion. Also discussed are strategies to overcome the barriers and promote passive house construction, offering a pathway toward sustainable building practices in Nepal.

Sources and determinants of exposure to indoor air pollutants in schools and hospitals: a review

Abstract Background Indoor Air Quality (IAQ) is an important determinant of health. This study aimed to review the available scientific evidence about indoor pollutants and determinants of exposure in several settings, in the scope of the K-HEALTHinAIR project. Methods The systematic review was performed for the sources, concentrations, and determinants of pollutants in schools in the European region for 2013-2023. Articles were selected for data extraction if they were epidemiological studies, reported primary data for indoor air and pollutant levels, and, identified sources and determinants of exposure. Results The main pollutants affecting IAQ in schools and hospitals were carbon dioxide, volatile organic compounds (e.g., formaldehyde, terpenes), particulate matter, nitrogen dioxide, radon, ozone, and carbon monoxide. Besides the outdoor environment, several important indoor sources of emissions were identified such as occupancy, resuspension, cleaning products. Ventilation was the main determinant influencing the indoor levels of pollutants, stressing the importance of good planning, installation, and maintenance of mechanical ventilation systems in buildings. Other determinants were the number of occupants and their activities, and the building characteristics (e.g., type of floor, location, etc). Conclusions Results revealed indoor air quality as a public health concern since several pollutants are present in indoor settings. The identification of sources and determinants of exposure is fundamental for determining preventive measures in a public health perspective. Acknowledgments: K-HEALTHinAIR team for screening and data extraction: Mariana Corda, Jose Fermoso, Alicia Aguado, Sandra Rodriguez, Hanns Moshammer, Mireia Ferri, Belén Costa, Leticia Pérez, Wojciech Hanke, Artur Badyda, Piotr Kepa, Katarzyna Affek, Nina Doskocz, Laura Martín, Oguz Mulayim, Cesar Mediavilla Martinez, Alba Gómez, Ruben González, Isaac Cano, Josep Roca, Simon de Leede.

Internal Combustion Engine Fault Detection Based on Random Convolutional Neural Networks

The internal combustion engine plays a very important role in many fields, and when the internal combustion engine fails, if it is not found in time, it may cause continuous damage to the internal combustion engine, and further affect the life of the entire mechanical system. To solve the above problems, this paper proposes fault detection of internal combustion engines based on a random convolutional neural network. The cylinder burst noise signal of the internal combustion engine is decomposed by a stationary wavelet, and the inclusion matrix is composed of partial decomposition coefficients. Using singular value theory, the singular value containing matrix is extracted as the characteristic of cylinder burst noise signal. The singular value is used as the input of a random convolutional neural network to train and identify faults. The AdaBound optimizer was then applied to adapt the learning rate to changes, thereby accelerating the weight update of the model. At the same time, the neurons in the structure are randomly deactivated by dropout technology to prevent complex collaborative responses to the training data, and the diagnosis results of each network model are integrated by Dempster synthesis rules. The experimental results show that the minimum mean square error of 0.00165 is achieved when there are 15 neurons in the hidden layer, and it gradually increases as the number of neurons increases. Therefore, it is determined that there should be 15 neurons in the hidden layer, and the trainLM algorithm is used. The decision factors for training, validation, cross-validation, and overall data are 0.98947, 0.98597, 0.97738, and 0.9801, respectively, all reaching the control accuracy of 0.95, which indicates that the random convolutional neural network proposed has a high accuracy for internal combustion engine fault detection. For the main bearing Y-directional force, as the gap increases, its fluctuation trend strengthens; the main bearing force changes within a small range at first, then increases sharply when the gap reaches 0.2[Formula: see text]mm, and some main bearings even experience significant impact loads.

Fault migration diagnosis method for rolling bearing based on 2D convolutional neural network

ABSTRACT In recent years, the development of mechanical systems has gradually become larger and more complex. As one of the most critical components of mechanical systems, the health status of rolling bearings is crucial for the safe and orderly operation of the entire mechanical system. Therefore, this paper constructs a transfer diagnosis model based on two-dimensional convolutional neural networks, achieving efficient and fast cross working condition fault transfer diagnosis of rolling bearings. In summary, this article takes rolling bearings as the research object and constructs a fault diagnosis model under working condition migration by combining two-dimensional convolutional neural networks with maximum mean difference. Based on the constructed model, experiments are designed and signal data is collected for model training, validation, and testing. This article aims to achieve cross working condition transfer diagnosis of rolling bearings by combining the constructed model with real data from the test bench, providing reference and guidance for cross domain fault diagnosis of rolling bearings in practical engineering. The research results indicate that the constructed transfer diagnosis model based on two-dimensional convolutional neural network has achieved efficient and fast fault transfer diagnosis of rolling bearings across working conditions. The accuracy of the model in the frequency domain of transfer under 6 working conditions is higher than 98.15%.

NMPC‐Based Control of Underactuated Spacecraft: A Neighboring Extremal Approach Extended to SE(3)

ABSTRACTThe present work revolves around employing the benefits of neighboring extremal approach in the nonlinear model predictive control of mechanical systems evolving on SE(3). In the NMPC process, necessary conditions of optimality are extracted based on some discrete‐time version of the equations of motion referred as LGVI and the obtained TPBVP equations are solved using simplified sensitivity derivatives by means of an indirect shooting method. Taking into consideration that the repetitive optimization process of the NMPC is time consuming, making its implementation challenging, a method of neighboring extremal is proposed here for recalculating the responses of the systems in scenarios that face some unexpected changes in their parameters or are encountering some last‐minute re‐planning schemes. Based on the existing responses of the system to the first set of initial conditions, the reaction of the system to the altered set of initial conditions can be reconstructed without the need to solve the whole optimization process from scratch by utilizing the features of the NE method. A spacecraft model evolving on SE(3) with actuation constraints confirms the efficiency of the whole process in term of accuracy versus computation burden. Forasmuch as actuator failure is a probable yet important event in aerial/spatial missions, the performance of the control system in dealing with such situations is examined. The algorithm robustness and its capability to compensate the effects of the actuator loss is checked.

Winch Traction Dynamics for a Carrier-Based Aircraft Under Trajectory Control on a Small Deck in Complex Sea Conditions

When the winch traction system of a carrier-based aircraft works under complex sea conditions, the rope and the tire forces are greatly changed compared with under simple sea conditions, and it poses a potential threat to the safety and stability of the aircraft’s traction system. The accurate calculation of the rope and tire forces of a carrier-based aircraft’s winch traction under complex sea conditions is an arduous problem. A novel method of dynamic analysis of the aircraft-winch-ship whole system under complex sea conditions is proposed. A multiple-frequency excitation is adopted to describe the complex sea conditions and the influences of pitching amplitude, and the rolling frequency on the traction dynamics of a carrier-based aircraft along the setting trajectory under complex sea conditions are studied. The advantages and disadvantages of a winch traction system with trajectory control and without trajectory control in complex sea conditions are analyzed. For realizing the trajectory control of the aircraft, the vector difference between the center of mass for the carrier-based aircraft and the position on the predetermined Bessel curve is calculated, so as to obtain the azimuth vector in the aircraft coordinate system. This research is innovative in the modeling of the whole system and the trajectory control of a carrier-based aircraft’s winch traction system under the complicated sea condition of the multi-frequency excitation. ADAMS (Automatic Dynamic Analysis of Mechanical System) is used to verify the correctness of the theoretical calculation for the winch traction. The results show that the complex sea environment has a certain influence on the winch traction safety of the aircraft; in the range of 10–15 s for the traction, the rope force amplitude of complex sea conditions under the multi-frequency excitation is 29.5% larger than that of the single-frequency amplitude, while the vertical force amplitude of the tire is 201.1% larger than that of the single-frequency amplitude. This research has important guiding significance for the selection of rope and tire models for a carrier-borne aircraft’s winch traction in complex sea conditions.

Constraint Realization‐Based Hamel Field Integrator for Geometrically Exact Planar Euler–Bernoulli Beam Dynamics

ABSTRACTIn this article, we first introduce a Hamel field integrator designed for a geometrically exact Euler–Bernoulli beam with infinite‐dimensional holonomic constraints, constructed using a Lagrange multiplier. This method addresses the complexities introduced by constraints, but the additional multiplier introduces a new degree of freedom and hence results in a system with mixed‐type partial differential equations. To address this issue, we further propose a constraint realization method based on perturbation theory for infinite‐dimensional mechanical systems within the framework of Hamel's formalism. This method circumvents the use of additional Lagrange multiplier, significantly reducing the computational complexity of modeling problems. Building on this, we construct a perturbed Hamel field integrator optimized for parallel computing and incorporate artificial viscosity to accelerate constraint convergence. While applicable to three dimensions, our method is demonstrated in a simplified context using planar Euler–Bernoulli beam examples to illustrate the effectiveness of the unified mathematical framework.

Evaluating the Effect of Adaptive Reuse in the Energy Performance of Historic Buildings: A Case Study from Türkiye

The building sector accounts for 30% to 40% of total energy consumption, and historic buildings play an important role in this proportion. Historical buildings that do not meet the required comfort conditions for the residents are adaptively reused, with various revisions. Recognizing the energy design of a historical building in its original condition and comparing the current situation can help create future solutions. This study examines the changes that a historic house in a hot climate zone in Türkiye experiences, from its original state up until the current situation. Energy analyses of the pre- and post-restoration situation are carried out, and the effect of adaptive reuse decisions on the energy performance of the building is investigated. A dynamic thermal simulation created with DesignBuilder was used to identify the energy use, carbon emissions, and thermal comfort. TM59 adaptive thermal comfort was used for the pre-restoration and the Fanger model for the post-restoration phase. This building, which was repurposed from a three-block residence, consists of a four-block hotel. Although the preservation of its original value is at the forefront, various structural changes were observed. The analysis demonstrates a higher occurrence of discomfort hours during summer compared to winter, consistent across both phases. Furthermore, energy consumption increased significantly, predominantly for heating, representing a doubling of energy use during the post-restoration phase. This is attributed to the building’s conversion into a hotel and the use of mechanical systems. Future research is required to develop strategies to reduce the energy consumption, carbon emissions, and discomfort hours while maintaining the value of the historic building and its materials.

Reduction by Symmetries of Contact Mechanical Systems on Lie Groups

Reduction by Symmetries of Contact Mechanical Systems on Lie Groups

Generalization of the Faraday problem for the mechanical system «reservoir – liquid» in the presence of a vertical periodic (sawtooth) disturbance

The generalization of Faraday's classic problem about the parametric resonance of the free surface during vertical oscillations of the tank according to the periodic law of the modulus of the cosine is considered. Regions of stability and instability are designed, which are compared with similar results for the classical Faraday problem. The behavior of the system is considered on the basis of a nonlinear multimode model, which describes the combined movement of the tank and fluid under the influence of external force excitation or kinematic disturbance.

Linearly Implicit Conservative Schemes for the Nonlocal Schrödinger Equation

This paper introduces two high-accuracy linearly implicit conservative schemes for solving the nonlocal Schrödinger equation, employing the extrapolation technique. These schemes are based on the generalized scalar auxiliary variable approach and the symplectic Runge–Kutta method. By integrating these advanced methods, the proposed schemes aim to significantly enhance computational accuracy and efficiency, while maintaining the essential conservative properties necessary for accurate physical modeling. This offers a structured approach to handle auxiliary variables, ensuring stability and conservation, while the symplectic Runge–Kutta method provides a robust framework with high accuracy. Together, these techniques offer a powerful and reliable approach for researchers dealing with complex quantum mechanical systems described by the nonlocal Schrödinger equation, ensuring both accuracy and stability in their numerical simulations.

Research on Artificial Self-Recovery Technology in Engineering Practice

With the development of mechanical systems in the industrial era, there is an increasing emphasis on the safety and reliability of mechanical equipment. The theory of artificial self-recovery technology has emerged, aiming to enable mechanical equipment to autonomously prevent and repair faults by simulating biological self-recovery mechanisms, thereby enhancing the safety and reliability of industrial production, reducing manual intervention, and promoting the intelligent development of manufacturing. The article mainly explores the application of artificial self-recovery theory in mechanical equipment, elaborating on the achievements in self-recovery technologies such as automatic balancing technology, compensation and self-protection technology for large systems, equipment health assistive technology, and active control technology for sealing devices and hydrostatic bearings, as well as self-repair and self-cleaning technologies. Finally, the paper looks forward to the future development of artificial self-recovery technology, believing that with technological advancements it will play an increasingly important role in the industrial field and promote the development of manufacturing towards self-recovery.