Mechanical Engineering Systems Research Articles

Shear Band Formation with Split Hopkinson Bar Experiments

The essence of dynamic failure is closely linked to dramatic shear deformations which often lead to the formation of adiabatic shear bands (ASB). Under high loading velocities and the subsequent rapid temperature increase, the localization of shear strain is crucial in view of safety issues of systems in mechanical and aircraft engineering, especially with respect to fast rotating components and diverse crash scenarios. In this research, we perform high speed impact tests at the split Hopkinson pressure bar (SHPB) setup and use particular hat-shaped specimen geometries that resemble the stresses and failure conditions at the component level.In the first step, we specify a notched specimen geometry using finite element (FE) simulations to ensure pure shear. Further, quasi-static compressive tests and a series of impact tests at high strain rates of 103−104s−1 are conducted on specimens manufactured from a fine-grain structural steel with the properties of S355. Optical microscopy and electron backscatter diffraction (EBSD) of the sheared zones unveil significant localization to maximal shear strains of about 0.9 accompanied by grain refinement by factors 5 to 14. The displacements across the surface of the specimens are captured with subset-based local digital image correlation (DIC) during the impact time, and serve as an objective to validate a viscoplastic constitutive relationship. More precisely, the deformation distribution is accurately reproduced by the widely recognized Johnson-Cook (JC) model, which features an enhanced description of damage evolution. Thus, combining experimental and characterization techniques, continuum mechanics and reasonable optimization strategies for the identification of model parameters provides an efficient approach for comprehensive insights into the strain localization behaviour and its impact on the mechanical performance of S355 under extreme strain rates and deformations.

Nagynyomású hidrogénatmoszférás kemence gyártása szénacélok elridegedésének vizsgálatához

Összefoglalás. A hidrogén tárolása iránti igény egyre növekszik, melynek oka az, hogy a hidrogén mint alternatív energiahordozó jelentős szerepet játszik a szén-dioxidkibocsátás-csökkentési törekvésekben. Azonban, az elridegedési folyamatok körében a hidrogén által előidézett károsodások jelentik a legkomolyabb problémát az acélokra nézve. Kutatómunkánk során egy nagynyomású hidrogénatmoszférás hőkezelésre alkalmas autoklávot terveztünk, majd gyártottunk. Az autoklávban P355 NH minőségű alapanyagból kimunkált szabványos Charpy-féle ütőpróbatesteket hőkezeltünk 150 °C-on, 40 bar túlnyomáson, 200 órán keresztül, tiszta hidrogén atmoszférában. A vizsgálatok eredményei alapján megállapítottuk, hogy a jelentős mértékű elridegedés a hidrogénezési folyamat után sem történt. Summary. The demand for hydrogen storage is growing. The primary reason for this is that hydrogen as an alternative energy carrier is playing a significant role in carbon reduction efforts as a fuel for road and maritime transport. In addition, hydrogen can be seen as a long-term flexible energy storage option. Hydrogen as an energy carrier is expected to play a significant role in residential and industrial use in the future. A first step in this development is the mixing of hydrogen with natural gas in small quantities. Increasing the share of hydrogen would not only reduce CO2 emissions, but would also facilitate the development of different hydrogen production methods and thus reduce production costs. The upper safety limit for hydrogen blending with natural gas is set by the national specification of the natural gas supply, possible material quality restrictions and the tolerance of the most sensitive equipment in the network. For this reason, the maximum allowable hydrogen content in natural gas is generally limited to 2,5%. However, among the embrittlement processes, hydrogen-induced damage is the most serious problem for both non-alloy and low-alloy steels. Atomic hydrogen diffuses in steel at high rates. The diffusion rate is orders of magnitude higher than that of other elements; this is due to the small atomic diameter of hydrogen. To investigate the degradation of different carbon steels in a high-pressure hydrogen atmosphere, a hydrogenation furnace was designed and fabricated at the Department of Materials Science and Engineering at BME. The hydrogenation furnace is the main part of a complex mechanical engineering system, for the operation of which the mechanical, heating, electrical supply, control and gas handling components are indispensable. The entire design process of the furnace was led by József Blücher, Professor Emeritus at BME, with the help of the engineers and technicians working on the project. Standard Charpy impact test specimens were machined from P355 NH grade material to test the embrittlement of carbon steels. The hydrogenation temperature was 150 °C, the internal overpressure in the chamber was 40 bar, the hydrogenation time was 200 hours and the atmosphere was hydrogen gas of purity 5.0. The impact test was carried out at -20, 0 and +20 °C. Based on the results of the tests, it can be concluded that there was no significant degree of degradation both before hydrogenation and after prolonged exposure to hydrogen. It can be concluded that the tube material used as a sample is suitable for operation in a hydrogen medium. Based on the analysis of the burst areas and impact values of the impact test specimens, it was concluded that hydrogen did not cause any observable damage to the test material.

Adaptable robotics for disaster response and search & rescue: Integration of deformable smart car design and pi control

In the context of disaster response and search and rescue operations, the need for adaptable and efficient robotic systems has become increasingly evident. This research paper addresses the evolving challenges in this domain by introducing a novel DIY deformable robot equipped with advanced PI (Proportional-Integral) control. The background of this study emerges from the growing urgency to enhance the capabilities of robotics in post-disaster scenarios, where navigation through complex terrains and the swift delivery of supplies are paramount.The primary objective of this paper is to develop and assess a versatile robotic platform using a multidisciplinary approach encompassing mechanical engineering, electronics, and control systems. The core of the investigation is a DIY deformable car consisting of two sensor-equipped containers and a linking module, making it well-suited for search and rescue missions. This paper contains the implementation of a PI control system to govern the robot's mobility and adaptability. This includes a detailed examination and demonstration of the PI control mechanism, encompassing its proportional and integral components. The actual results also indicate that the smart car controlled by the PI controller has better performance in both stability and speed.

Study of high power steam turbine forced oscilla-tions near operating frequency

Problem. Forced oscillations of the turbine unit-foundation-base system near the operating frequency are considered. Goal. The purpose of the work is to study forced vibrations of the turbine unit-foundation-base system near the operating frequency range to assess the vibration state of the system and determine the causes of increased vibrations. The object of the study is the turbine unit-foundation-base system. The system consists of a single reinforced concrete foundation on which a steam turbine and generator are installed. The subject of the study is the amplitude-frequency dependences of forced oscillations of the turbine unit-foundation-base system. Methodology. The study was carried out using vibration methods and the finite element method. Also worth noting is the use of original methods developed directly by the author for constructing models of complex mechanical engineering systems. Results. As a result of the research, three-dimensional finite element models of low-pressure cylinder housings, the foundation and the entire turbine unit-foundation-base system were built. The conducted research allows us to draw a conclusion regarding the reasons for the increased level of vibration near the operating frequency and directions for their prevention. Originality. Regarding the methods for constructing a model of the turbine unit-foundation-base system, it should be noted that they are unique. The features of the applied modeling method make it possible to take into account the variable interaction between the low-pressure cylinder bodies and the foundation. Fixpoints are also taken into account. Previous studies of the turbine unit-foundation-base system did not allow us to determine the causes of increased vibrations of the shaft line supports. Practical significance. The results of the work relate to direct practical application. As a result of the work, conclusions were drawn about the ways to increase the vibration reliability of the turbine unit-foundation-base system and further directions for research.

Mode decomposition and spatial propagation regularity analysis of cavitation variation on a twisted hydrofoil

The cavitation phenomenon can induce non-uniformity in the fluid, impacting fluid dynamic performance. This paper focuses on the cavitation shedding of the Delft Twist 11 hydrofoil. First, the reliability of numerical simulations is verified by computational fluid dynamics results. Utilizing the variational mode decomposition method, the cavitation signals on two cavitation paths are decomposed. Finally, the cavitation pulsation tracking network method is proposed to extensively investigate the spatial propagation patterns of cavitation signals at various sections above the twisted hydrofoil. The results reveal that typical frequencies at different monitoring planes are 30, 58, and 88 Hz. The corresponding amplitude analysis at these frequencies provides insight into the spatial propagation and attenuation process of cavitation vortices shedding. This study offers a novel perspective for a deeper understanding of cavitation mechanisms. Simultaneously, this provides references for enhancing the performance of mechanical engineering systems, reducing energy consumption, and improving structural reliability.

‘Adequate amount of data’‐driven system design theory: how far can we know/control/protect?

In contrast to the natural tendency of many researchers to focus on collecting as much data as possible, Assistant Professor Tomonori Sadamoto, from the Department of Mechanical Engineering and Intelligent Systems at the University of Electro-Communications in Japan, believes that the pursuit of big data is not always desirable. He is promoting a shift towards the acceptance of a “adequate amount of data”-driven system design theory. Sadamoto is concentrating on data-driven methodologies and their application in social systems. Through key international collaborations with colleagues at leading institutions, he is advancing his research. His work on data-driven methodologies focuses on interdisciplinary studies that combine machine learning and control theory. His more applied work primarily falls within the realm of smart grids. In his projects, he formulates his questions mathematically from the perspective of control theory. For instance, Sadamoto has developed a novel mathematical tool known as the VARX (vector autoregressive with exogenous input) framework, which facilitates the tractable analysis of dynamic systems. Using this new tool, he has developed data-dependent system identification analyses when only an “insufficient amount of data” is available. Furthermore, for the first time, Sadamoto was able to demonstrate that the informativeness of data in a certain class of dynamic output controller design is equivalent to the identification of the target system. His efforts are aimed at expanding the horizons of these novel control theories into the field of smart grids.

Analysis of the application of automated systems in mechanical engineering

Introducing off-the-shelf solutions for automated machine-building systems causes some difficulties related to attempts to change the algorithms of process participants, and also to the need to duplicate information transfer chains, as most systems operate autonomously within certain production units. The author proposes a new approach to organizing the interaction of automated systems of machine-building enterprises. Effective implementation of automated systems is possible only based on investigation and optimization of machine-building enterprise activities, and based on analysis and quality assessment of functions performance by participants of different enterprise processes. The method comprises identifying the process for preparing production and output of finished products, and processes of lower levels (auxiliary), but necessary for implementing the main, and functionally related to it. The main functional process defines a time parameter and auxiliary processes accompanying the main process. These auxiliary processes are financial, material, personnel, and organizational support processes. The article contains the basic principles of creating a complex of automated machine-building systems. The article analyzes the role and place of each of the main functional automated systems. The author gives considerations on the sequence of implementation of the project of digitalization of machine-building production.

Application of maximum statistical entropy in formulating a non-gaussian probability density function in flow uncertainty analysis with prior measurement knowledge

In mechanical, civil and chemical engineering systems the accuracies of flow measurement instruments is conventionally specified by certified measurement capabilities (CMCs) that are symmetric, however it is physically possible for some flow instruments and equipment to exhibit asymmetric non-Gaussian behaviour. In this paper the influence of non-Gaussian uncertainties is investigated using direct Monte Carlo simulations to construct a probability density function (PDF) using representative non-Gaussian surface roughness data for a commercial steel pipe friction factor. Actual PDF results are compared and contrasted with a symmetric Gaussian PDF, and reveal inconsistencies in the statistical distributions that cannot be neglected in high accuracy flow measurements. The non-Gaussian PDF is visualized with a kernel density estimate (KDE) scheme to infer an initial qualitative shape of the actual PDF using the approximate locations of the normalized peaks as a initial metrologist estimate of the measurement density. This is then utilized as inputs in a maximum statistical entropy functional to optimize the actual non-Gaussian PDF using a nonlinear optimization of Lagrange multipliers for a mathematically unique PDE. Novelties in the present study is that a new methodology has been developed for statistical sampling from non-monotonic non-Gaussian distributions with accompanying Python and Matlab/GNU Octave computer codes, and a new methodology for utilizing metrologist's expert prior knowledge of PDF peaks and locations for constructing an a priori estimate of the shape of unknown density have been incorporated into the maximum statistical entropy nonlinear optimization problem for a faster and more efficient approach for generating statistical information and insights in constructing high accuracy non-Gaussian PDFs of real world messy engineering measurements.

Artificial Intelligence (AI) Techniques for Intelligent Control Systems in Mechanical Engineering

In mechanical engineering, control systems are essential to the dependable and effective operation of mechanical equipment and processes. The advantages of integrating AI into control systems include energy savings, greater product quality, increased process effectiveness, and adaptive and predictive control. Various AI approaches, including machine learning methods like decision trees, neural networks, and support vector machines, are extensively studied for system identification, modelling, and adaptive control. AI techniques enhance these applications' control performance, energy efficiency, problem discovery, and maintenance. The methodology uses metaheuristic techniques for creating intelligent control systems, such as simulated annealing, ant colony optimisation, and harmony search. With the help of these algorithms, the solution space is efficiently searched for ideal control strategies while avoiding local optima. The importance of evaluating the learned control algorithm on a different dataset or running experiments to determine its performance is also underlined. Performance assessment benchmarks for tracking error, settling time, overshoot, and energy efficiency are provided in the results section. This paper investigates the use of AI techniques in mechanical engineering intelligent control systems. The paper also offers areas for additional research in intelligent control systems and identifies research directions for future advancements.

Special Section on Uncertainty Quantification and Management in Nonlinear Dynamical Systems in Aerospace and Mechanical Engineering

Abstract n/a

On students learning experience of fluid power engineering – Impact of simulation software

The School of Engineering and Technology of Central Queensland University focuses on the continuous development and innovation of best learning and teaching practices to increase student retention and improve their learning experiences. Face to face and distance learning (teaching delivery models) are fundamental aspects of providing quality support to the students’ learning. One incredibly important aspect of students’ learning is to provide relevant industry-related projects and applications of relevant simulation software to mimic the systems. This paper develops students’ essential problem-solving skills and control strategies of fluid power systems in mechanical engineering through the Master of Engineering programs by employing Simulink, SimScape Fluid applications in Matlab software and Energy Plus with Design Builder. The main focus is to ensure that the students achieve the required skills of building fluid circuit models based on physical connections that directly integrate with appropriate symbols and modelling paradigms in fluid power applications to model the appropriate physical models to mimic the industrial fluid power projects selected. The basic ideas and content are to be delivered through weekly lectures and tutorial sessions, and the students’ skills in fluid power systems and software are developed and monitored through weekly workshops scheduled for all projects. The key outcomes of this study are the level of understanding of fluid power systems, development of simulation skills using the software indicated, interpretation of the results the students have obtained and validations of those results. The students must show they have developed appropriate problem-solving skills using simulation software, professional presentation and effective team-building skills. As the students develop appropriate problem-solving and engineering practice skills, their satisfaction and feedback rates improve significantly.

Preface to “Model Predictive Control and Optimization for Cyber-Physical Systems”

The concept of cyber-physical systems (CPSs) in electrical, civil and mechanical engineering is closely related to Smart Grids and Smart Cities, based on advanced computing technologies used for monitoring, control and communication [...]

Parametric Analysis of the Nonlinear Dynamics of a Cracked Cantilever Beam

Abstract Structural damage occurs in a variety of civil, mechanical, and aerospace engineering systems, and it is critical to effectively identify such damage in order to prevent catastrophic failures. When cracks are present in a structure, the breathing phenomenon that occurs between crack surfaces typically triggers nonlinearity in the dynamic response. In this work, in order to thoroughly understand the nonlinear effect of cracks on structural dynamics, two modeling approaches are integrated to investigate the crack-induced nonlinear dynamics of cantilever beams. First, a modeling method referred to as the discrete element (DE) method is employed to construct a model of a cracked beam. The DE model is able to characterize the breathing phenomenon of cracks. Next, a simulation technique referred to as the hybrid symbolic-numeric computational (HSNC) method is used to analyze the nonlinear response of the cracked beam. The HSNC method provides an efficient way to evaluate both stationary and nonstationary dynamics of cracked systems since it combines efficient linear techniques with an optimization tool to capture the system’s nonlinear response. The proposed computational platform thus enables efficient multiparametric analysis of cracked structures. The effects of crack location, crack depth, and excitation frequency on the cantilever beam are parametrically investigated using the proposed method. Nonlinear features such as subharmonic resonance, nonstationary motion, multistability, and frequency shift are also discussed in this paper.

The place and role of automated systems in mechanical engineering

Introducing off-the-shelf solutions for automated machine-building systems causes some difficulties related to attempts to change the algorithms of process participants, and also to the need to duplicate information transfer chains, as most systems operate autonomously within certain production units. The author proposes a new approach to organizing the interaction of automated systems of machine-building enterprises. Effective implementation of automated systems is possible only based on investigation and optimization of machine-building enterprise activities, and based on analysis and quality assessment of functions performance by participants of different enterprise processes. The method comprises identifying the process for preparing production and output of finished products, and processes of lower levels (auxiliary), but necessary for implementing the main, and functionally related to it. The main functional process defines a time parameter and auxiliary processes accompanying the main process. These auxiliary processes are financial, material, personnel, and organizational support processes. The article contains the basic principles of creating a complex of automated machine-building systems. The article analyzes the role and place of each of the main functional automated systems. The author gives considerations on the sequence of implementation of the project of digitalization of machine-building production.

Department of Mechanical Systems Engineering, Graduate School of Engineering, Tokyo University of Agriculture and Technology

Department of Mechanical Systems Engineering, Graduate School of Engineering, Tokyo University of Agriculture and Technology

Physics-Guided Real-Time Full-Field Vibration Response Estimation from Sparse Measurements Using Compressive Sensing.

In civil, mechanical, and aerospace structures, full-field measurement has become necessary to estimate the precise location of precise damage and controlling purposes. Conventional full-field sensing requires dense installation of contact-based sensors, which is uneconomical and mostly impractical in a real-life scenario. Recent developments in computer vision-based measurement instruments have the ability to measure full-field responses, but implementation for long-term sensing could be impractical and sometimes uneconomical. To circumvent this issue, in this paper, we propose a technique to accurately estimate the full-field responses of the structural system from a few contact/non-contact sensors randomly placed on the system. We adopt the Compressive Sensing technique in the spatial domain to estimate the full-field spatial vibration profile from the few actual sensors placed on the structure for a particular time instant, and executing this procedure repeatedly for all the temporal instances will result in real-time estimation of full-field response. The basis function in the Compressive Sensing framework is obtained from the closed-form solution of the generalized partial differential equation of the system; hence, partial knowledge of the system/model dynamics is needed, which makes this framework physics-guided. The accuracy of reconstruction in the proposed full-field sensing method demonstrates significant potential in the domain of health monitoring and control of civil, mechanical, and aerospace engineering systems.

On Research and Training in Machinery Diagnostics in Engineering Education

Abstract We have a decades-long tradition of examining the operation and maintenance issues of mechanical engineering systems at the Faculty of Engineering at the University of Debrecen. In the last decade, technical diagnostic research has come to the fore, especially bearing diagnostics, a lot of experience has been gathered in this field and many results have been achieved. With the development of the tools of technical diagnostics, the presentation of the topic at all levels of engineering education, and the establishment of industrial relations, an environment has been created in which it is possible to answer the current questions on the topic. Technical diagnostics, which is largely applied informatics (and mathematics) – together with several other engineering topics – also raises educational questions, which we intend to answer by transforming some elements of the training.

Magnetized squeezing nanofluid flow with viscous heating and Robin boundary conditions: A Buongiorno nanofluid model

The flow of fluid that occurs when two parallel disks are squeezed together has applications in compression, the processing of polymers, the production of plastics, injection modeling, and lubrication systems. In this paper, the unsteady squeezing flow and heat transport of nanoliquid that is subjected to convective thermal boundary conditions and viscous heating have been studied numerically. This study was inspired by the exploration of the thermophysical properties of magnetic nanoparticles in squeezing tribology. The flow between two horizontal parallel disks is accounted for where the upper disk is non-static when the lower disk is fixed. The powerful Runge–Kutta method-based shooting scheme is utilized to solve the assumed problem. The influence of pertinent key parameters on involved fields is visualized graphically and scrutinized. It is exhibited that the haphazard motion of NPs contributes highly to the enhancement of thermal and concentration fields. Also, the Robin boundary conditions affect flow fields significantly. Intensifying the Brownian motion effect enhances NPs’ concentration. Radial velocity is damped in the core region with stronger magnetic field. The mass transport rate is diminished, and the heat transmission rate is enhanced. The computations are relevant to smart nano-tribological systems in mechanical and aerospace engineering.

A Deep Learning-Based Approach for the Identification of a Multi-Parameter BWBN Model

A restoring-force model is a versatile mathematical model that can describe the relationship between the restoring force and the deformation obtained from a large number of experiments. Over the past few decades, a large body of work on the development of restoring-force models has been reported in the literature. Under high intensity cyclic loadings or seismic excitations, reinforced concrete (RC) structures undergo a wide range of hysteretic deteriorations such as strength, stiffness and pinching degradations. These characteristic behaviors can be described by the multi-parameter Bouc-Wen-Baber-Noori (BWBN) model, which offers a wide range of applicability. This model has been applied for the response prediction and modeling restoring-force behavior in structural and mechanical engineering systems, by adjusting the distribution range of this model’s parameters. However, a major difficulty in utilizing the multi-parameter BWBN model is the parameters’ identification. In this paper, a deep neural network model is used to estimate the hysteresis parameters of the BWBN model. This model is one of the most versatile and widely used general hysteresis models that can describe the hysteretic behavior of RC columns. The experimental data of the RC columns used in this paper are collected from the database of the Pacific Earthquake Engineering Research Center (PEER). Firstly, the hysteretic loop obtained from a physical experiment is described by the BWBN model, and the parameters of the BWBN model are identified via a genetic optimization algorithm. Then a neural network is established by a backpropagation (BP) algorithm for associating the identified BWBN model parameters with physical parameters of the RC column. Finally, the regression analysis of the identified parameters is carried out to obtain the regression characteristics of the RC columns. The trained neural network model can directly identify the parameters of BWBN model based on the physical parameters of RC columns, and is effective and computationally efficient for multi-parameter BWBN model identification. The proposed approach overcomes the difficult problem of identifying the parameters of BWBN model and provides a promising approach for a wider application of this multi-parameter hysteresis model.

Application of Artificial Intelligence in Mechanical Engineering

The use of artificial intelligence (AI) is becoming more prevalent across many industries. Examples include intelligently based control, intelligently based mechanical systems, pattern recognition-based systems, and knowledge processing. Method/Statistical Analysis: In this paper, an extensive review was conducted on the applications of ANN in intelligent mechanical engineering systems, including fault diagnosis in machines, mechanical structure analysis, and geometry modelling of mechanical structures, mechanical design, and its optimization. Findings: The adaptation of artificial neural networks (ANN), particularly in the field of mechanical engineering, is still in its early stages of development. This paper highlights the different ways artificial neural networks (ANNs) are used in intelligent-based systems, as well as the potential for reducing costs and time and obtaining more efficient systems for mechanical-based design and defect detection. Application/Improvements: This work will be improved in the future by adding more AI applications to the design of mechanically based systems.