Mechanical Engineering Problems Research Articles

Software Of Air Compressor Calculator

This paper proposes the development of software using HTML used in reciprocating air compressor studies in the undergraduate level of engineering students. This software helps the students understand the different types and different factors associated with reciprocating air compressors. A small study was conducted to merge coding with mechanical engineering problems. The data of different types of reciprocating air compressors are collected and layouts of these types are generated using HTML. The layout is then programmed with the expressions collected for reciprocating air compressors. This software enables the user to understand the overall aspects of the Air compressor and all its related parameters. Ultimately, this project seeks to empower the students to understand the different parameters of different types of reciprocating air compressors by providing a seamless and intuitive software experience. Key Words: software, reciprocating, compressor, layout, data, volumetric efficiency.

Contra-hormonic generalized fuzzy numerical scheme for solving mechanical engineering problems

Contra-hormonic generalized fuzzy numerical scheme for solving mechanical engineering problems

A metaheuristic-based comparative structure for solving discrete space mechanical engineering problem

A metaheuristic-based comparative structure for solving discrete space mechanical engineering problem

Brain-inspired spiking neural networks in Engineering Mechanics: a new physics-based self-learning framework for sustainable Finite Element analysis

AbstractThe present study aims to develop a sustainable framework employing brain-inspired neural networks for solving boundary value problems in Engineering Mechanics. Spiking neural networks, known as the third generation of artificial neural networks, are proposed for physics-based artificial intelligence. Accompanied by a new pseudo-explicit integration scheme based on spiking recurrent neural networks leading to a spike-based pseudo explicit integration scheme, the underlying differential equations are solved with a physics-informed strategy. We propose additionally a third-generation spike-based Legendre Memory Unit that handles large sequences. These third-generation networks can be implemented on the coming-of-age neuromorphic hardware resulting in less energy and memory consumption. The proposed framework, although implicit, is viewed as a pseudo-explicit scheme since it requires almost no or fewer online training steps to achieve a converged solution even for unseen loading sequences. The proposed framework is deployed in a Finite Element solver for plate structures undergoing cyclic loading and a Xylo-Av2 SynSense neuromorphic chip is used to assess its energy performance. An acceleration of more than 40% when compared to classical Finite Element Method simulations and the capability of online training is observed. We also see a reduction in energy consumption down to the thousandth order.

Increase of the Efficiency of Heat Supply Units of the CHP Plants Due to the Choice of Rational Heat Release Modes

The possibilities of increasing the efficiency of heat supply turbines of CHPPs due to the choice of rational modes of operation of network water heaters are analyzed. With the help of a software and computing complex developed at the Institute of Mechanical Engineering Problems of the National Academy of Sciences of Ukraine and adapted by the authors to the operating conditions of CHPP generating equipment with one or two network heaters, a set of calculation studies of various ways of connecting them depending on the outdoor air temperature is conducted in the paper. Areas of positive effect associated with increase in the electric power of the power plant have been established. The calculation study was carried out at typical power plant water consumption from 1000 t/h to 4500 t/h, in the range of changes in the outdoor air temperature from -11 ºС to 10 ºС (heating season) and more than 10 ºС (hot water supply). The load change of the power unit was carried out due to the consumption of fresh steam at a constant pressure and temperature at the turbine inlet. As shown by the results of the operation of the T-100/120-130 heat supply turbine in operating conditions with one or two heat supply steam selections, in the area of positive outdoor air temperatures above 2 ºС, it is advisable to use one lower selection (with the upper one turned off) for all network water consumption. At the same time, additional electric power in the area of outdoor air temperatures above 6ºС can be from 0.25 MW to 2.15 MW. However, when the outdoor air temperature is less than 2 ºС, work with one lower heating selection becomes irrational. From the point of view of choosing rational modes of operation of turbine plants, the most important are the results of determining the optimal distribution of heat load between network heaters. The gain in electrical power of the turbine can be up to 2.46 MW in the nominal mode of operation with two heaters, and up to 7.84 MW in comparison with the use of single-stage heating. The nature of the influence of the distribution of the heat load indicates that during deprivation from the instructional uniform distribution of the heat load between network heaters, it is possible to obtain additional electricity.

Application of Subtraction Average-Based Optimizer to Selected Electrical and Mechanical Engineering Problems

Application of Subtraction Average-Based Optimizer to Selected Electrical and Mechanical Engineering Problems

Ensuring safe working conditions during manual abrasive processing of welded joints of frame elements of transport vehicles

One of the essential problems of current mechanical engineering is the re-duction of noise and vibration of technological processes. Excessive noise not only leads to the developing of operator illnesses, but is also an indicator of the poor quality of the final product. The article deals with the problem of reducing hazardous and harmful production factors during manual processing of welded joints of vehicle body elements. An obligatory stage after welding is the mechanical abrasive treatment of welded joints to remove stress concentrators and straighten the profile. The existing technology uses a hand-held grinder, which creates a lot of noise and vibration and reduces the accuracy of welding seams. It is shown that with the existing technology, the noise levels are exceeded in the frequency range of 250…8000 Hz and reach 20 dB. To reduce the noise level, theoretical results were obtained for the dependences of the vibrational velocities of the abrasive tool and the results of experimental studies of the noise and vibration spectra during manual abrasive processing of welded seams of body structures. Obtained mathematical models enable the calculation of sound pressure level for abrasive processing. A scheme of a device is proposed that makes it possible to reduce the level of sound pressure during manual abrasive processing of welded seams of frame structures and its advantage over vibration damping coatings is presented. The combination of an integrated approach and protective equipment can decrease the sound pressure level by as much as 30 dB.

Modeling of Ultrasonic Flaw Detection Processes in the Task of Searching and Visualizing Internal Defects in Assemblies and Structures

Introduction. Inverse problems are a specific type of tasks where the consequences of phenomena are studied to identify their causes. They are widely used in scientific studies, specifically, those dealing with large amounts of experimental data. In the presented paper, inverse problems in mechanical engineering and structural diagnostics are considered. These areas require precise methods to identify internal defects in various materials, which can be critical to ensure the safety and efficiency of technical structures. Despite the many flaw detection methods available, there is a need for innovative developments that can provide higher accuracy and efficiency. This study integrates different scientific methods and technologies. It opens up new perspectives in nondestructive testing for the detection of internal defects in various materials and structures. Its objective is to develop and implement nondestructive testing methods based on a neural network device to improve the accuracy of defect identification, as well as to build a neural network model and evaluate its effectiveness for the refinement of ultrasonic visualization of internal defects in solid materials. In this regard, the task to be solved is to create a reliable tool for accurate visualization of sizes, shapes, location and orientation of internal defects in various materials.Materials and Methods. The technique of determining the geometric parameters of defects in materials through nondestructive testing is used. The approach combining modeling of ultrasonic wave propagation in acoustic medium and artificial neural network technologies is applied. This approach identifies nonlinear relationships between the geometry of defects and the amplitude-frequency and amplitude-time data obtained during signal analysis. Artificial neural networks are a model that can be trained on examples, which provides for an effective solution to problems that are difficult to express in traditional forms. The study uses the finite difference method in the time domain. It is applied to identify and visualize internal defects in materials using ultrasonic nondestructive testing and convolutional generative neural networks.Results. A convolutional neural network has been developed to visualize internal defects using ultrasonic nondestructive testing techniques. This neural network successfully determines the size of defects, their location, shape and orientation with high accuracy and reliability.Discussion and Conclusion. The authors highlight the key influence of defect size on the accuracy of ultrasonic imaging in various scenarios. The validation of the model for three different cases of defects with different mechanical parameters has shown that for successful visualization of defects, the wavelength of the ultrasonic pulse must be ten times smaller than the size of the defect. When analyzing the impact of defect size on the accuracy of the neural network, it is found that the visualization error increases for defects of smaller size. It has also been found that the relative speed of sound in materials has a greater effect on the accuracy of the method than the relative density of the material. Based on the results obtained by the authors, it can be argued that the developed methods and technical solutions are of great importance for future research in the field of flaw detection. They have significant potential for scientific and practical applications.

Моделирование эрозионного износа титанового сплава высокоскоростным потоком частиц

Introduction. Predicting solid particle erosion (SPE) in gaseous flow and managing its intensity is still a relevant problem in mechanical engineering. It requires the development of a general modeling methodology, which also depends upon many special cases studying various physical processes. Such studies should also include verification analysis, process parameters and model sensitivity studies. Mainly computational fluid dynamics and finite element analysis (and mesh-free methods such as smooth particle hydrodynamics or similar) are used to simulate the erosion process. Papers focused on CFD simulation of solid particle erosion of metal alloys are widely presented, but most of it is associated with relatively low or medium particle velocities (< 100–150 m/s) and is close to uniform diameter distribution. This paper presents a CFD study of Ti6Al4V titanium alloy SPE at relatively high particle velocities and sufficiently non-uniform unimodal particle diameter distribution. The paper also studies the turbulence model influence and particle shape effect which appears as a “shape factor” coefficient in the particle drag model. Methods. The heterogenous flow simulation was based on the Reynolds-averaged Navier-Stokes formulation, where the particles, according to Euler-Lagrange formulation, were simulated as mathematical points with corresponding properties. The influence of turbulence models, such as k-epsilon standard, RNG k-epsilon, and a relatively new Generalized equation k-omega (GEKO) model and its coefficients were also studied. Oka and DNV erosion models were also compared based on the general sample mass loss and more specific erosion intensity profile criterions. The simulation results were compared to the lab-scale experimental results. Results and discussion. It is shown that neither erosion intensity profile or sample mass loss do not depend upon the turbulence model choice or GEKO parameters variation. As expected, erosion is dependent on the erosion model and its coefficients. A notable influence of the shape factor is shown. As the drag coefficient increased due to the particle shape, the erosion intensity decreased and the erosive profile on the surface also changed due to the changing velocity and diameter distribution of the heterogenous flow. It is expected that such results would be useful not only for erosion prediction in all areas of mechanical engineering, but also for wear management in mechanical assemblies and shot peening / shot peen forming management and simulation.

Application of Modified Adaptive Bats Sonar Algorithm with Doppler Effect and Levy Flight (MABSA-DELF) to Optimize Mechanical Engineering Problems

This paper describes the application of the Modified Adaptive Bats Sonar Algorithm with Doppler Effect and Levy Flight (MABSA-DELF) to mechanical engineering design optimization issues. MABSA-DELF is a new algorithm that employs Doppler Effect and Levy Flight theory to improve the position of the transmitted bats’ beam. This project served as a showcase for the superior performance of MABSA-DELF. It was created using the MATLAB software’s computer simulation method. MABSA-DELF demonstrated a superior ability to solve engineering design problems in the fields of business, mechanical/manufacturing engineering, and electrical engineering. MABSA-DELF’s result are compared to those of other established algorithms.

Accelerating the computational process for analyzing dynamic thermal behavior of an innovatively designed longitudinally-graded sandwich plate: Deep-learning approach

Employing the solutions through artificial intelligence (AI) to solve mechanical engineering problems can pave the way toward accelerating the computation process, especially for those engaged with the dynamic nature of the time-variable systems. In this regard, this article attempts to employ deep neural networks (DNN) for the coupled thermoelasticity assessment of the longitudinally sandwiched plates exposed to intensive thermal shock. The left and right face sheets of the sandwich plate are enriched with graphene platelets (GPLs) along the longitudinal direction. The thermal shock is considered as the instantaneous heat flux applied in the axial direction. Elasticity theory is employed for deriving the governing equations in an exact template and differential quadrature approach (DQA) in conjunction with the Laplace transform plays the role of solution approaches to attain the thermoelastic reaction of the sandwich system at training points. Then, the DNN utilizes these points to adjust a predictor mechanism for analyzing the behavior of the sandwich system in every load-exertion circumstances. Additionally, the DNN would be optimized by a brand-new metaheuristic algorithm according to some error estimation criteria to minimize the error of regression. The mentioned solution is validated by comparing its performance with the outcomes of the published literature. This study provides a novel platform to use the great potential of AI-based methods in solving dynamic thermoelastic problems in the world of solid structures.

Artificial intelligence and its relevance in mechanical engineering from Industry 4.0 perspective

ABSTRACT In the third industrial revolution (IR), mechatronics engineering has emerged, which is the merger of mechanical and electronics engineering along with information technology to an extent. In the current industrial revolution (fourth), the amalgamation of mechanical with Artificial intelligence (AI) has been used to simulate human behaviour in machines. In the earlier decades, the application of AI in mechanical engineering has expedited and the increased volume of related research papers is a proof of this fact. The broad domain of mechanical engineering has a lot of potential for machine learning applications in order to detect errors, minimise rejection, improve product quality, optimise a system, and so on, as it has not been intensely explored although. This article’s goal is to give a structured evaluation of works that employ vision systems and to attract researchers to analyse its potential integration with machine learning (ML) techniques from the industry 4.0 viewpoint. Current trends and research gaps in the area of manufacturing in collaboration with ML are also discussed at appropriate places. A general procedure to carry out research in the field of mechanical engineering is also presented. The results of various studies highlighted in this paper confirm that the implementation of AI in ME has provided an optimum solution to many mechanical problems that improved product quality and minimised the associated cost. Finally, the authors have led a direction to empower researchers in order to explore various areas in mechanical engineering from Industry 4.0 and AI perspective and enable possible topics for future research. The article concludes by providing challenges that may be faced in the coming future about the use of machine learning in mechanical engineering problems.

The problem of reduce description in chemical kinetics

A comprehensive comprehension of the concept of the gradual unchanging manifold (GUM) gives rise to numerous techniques for reducing models (TRM) in dissipative systems of chemical kinetics for mechanical engineering problems. This research calculates and contrasts the unchanging solutions for multi-route systems. The TRM, such as the Intrinsic Low Dimensional (ILDM), Quasi Equilibrium Manifold (QEM), and Spectral Quasi Equilibrium Manifold (SQEM), are utilized to construct the gradual unchanging manifolds, which function as primary approximations to GUM. By employing the Method of Invariant Grid (MIG), the enhancements of QEM solutions are computed imposing the effect of activation energy. Our discoveries suggest that we could assess each route independently rather than taking into account the entire mechanism. Local sensitivity analysis is conducted using the SimBiology toolbox of MATLAB. To compare TRM, the calculation period is presented in tabular form.

"FFT-ASSISTED SOLUTION FOR THE EIGENSTRESS PROBLEM IN AN INFINITE ELASTIC MEDIUM "

Finding the distributions of eigenstresses induced by eigenstrains regardless of their type is a fundamental problem in mechanical engineering, described by complex mathematical models. Analytical solutions exist only for a small number of particular distributions of eigenstrains. This paper advances a numerical solution for the eigenstresses due to arbitrary distributions of eigenstrains in an infinite space. The imposed discretization transforms the continuous problem space into a set of adjacent cuboids, each characterized by a single value calculated analytically in a chosen point, usually the cuboid centre. In this manner, continuous functions are replaced in the mathematical model by sets of values calculated in discrete points, which, if the discretization is fine enough, replicate well the continuous distributions. The contribution of the uniform eigenstrains from a specific cuboid, to the eigenstresses in the calculation point, expressed analytically in the literature, is used as a starting point. To reduce the high computational requirements for superposition, state-of-the-art spectral methods for the acceleration of convolution products are applied. A Matlab computer program was developed to implement the newly advanced method. The case of a cuboid containing uniform dilatational eigenstrains was first simulated for validation purposes. Small deviations from the analytical solution can be observed near the inclusion boundary, but their magnitude decreases with finer meshes, suggesting it’s a discretization related error. The results were then extended by considering radially decreasing eigenstrains inside an ellipsoid.

An object‐oriented approach toward two‐dimensional engineering mechanics

AbstractThe present work proposes an object‐oriented approach for the solution of problems in two‐dimensional engineering mechanics. New classes (in the terminology of object‐oriented programming) are proposed and implemented in the popular programming language ‘Python.’ These classes, along with the symbolic computation module of Python ‘sympy,’ enable one to reduce the solution of a large class of problems in undergraduate engineering mechanics to a set of Python statements. These statements are definitions of objects as instances of the proposed classes and calling of their relevant methods. If the thought process in the manual solution is represented by a flowchart, the present work can be viewed as a flowchart translator. As all the calculations are automatically performed, the present work relieves the student from the burden and fear of lengthy calculations. Different diagrammatic representations of velocity, acceleration, shear force, and bending moments are included for better physical understanding. It is believed that the proposed simple implementation would make problem‐solving and the learning process more enjoyable. Apart from a standard PC, the proposed work runs on a single‐board computer (tested in raspberry pi 4) and on an Android mobile device. A student feedback study ascertains the acceptability of the proposed method.

Spiking recurrent neural networks for neuromorphic computing in nonlinear structural mechanics

The present study aims to introduce an AI algorithm suitable for neuromorphic computing to solve Boundary Value Problems in Engineering Mechanics. Following the trend of sustainable Artificial Intelligence (AI), Spiking Neural Networks (SNNs), known as the third generation of neural networks, are proposed for developing surrogate models for mechanical tasks. SNNs are inherently recurrent and communicate through sparse signals consuming only a fraction of the energy needed by artificial neural networks (ANNs) of the second generation such as current deep learning methods. To take advantage of the energy-efficient spike models, an autoencoding strategy is proposed to convert the real-valued data at a certain time step to its binary spike representation which allows the model to be used for regression tasks. Further, we propose a hybrid model consisting of the spiking variant of the Legendre Memory Unit (LMU), spiking recurrent cells, and classical dense transformations to compute the nonlinear (physically and geometrically) response of shock wave-loaded plate elements. The inclusion of spikes enables the proposed model to be deployed on neuromorphic hardware, such as the Loihi chip. Thus, for a series of both training and validation experiments, a detailed comparison of the proposed hybrid model and its second-generation counterpart in terms of energy consumption and accuracy is presented.

USE OF ORDINARY DIFFERENTIAL EQUATIONS APPLIED TO MECHANICAL AND ELECTRICAL PROBLEMS WITH FIRST ORDER LINEAR SYSTEMS

In recent years there has been an increased interest in the study of mathematics applied to engineering to study aspects of its teaching. In the present research work, a compilation of analytical concepts of differential equations applied to the solution of problems of electrical circuits, a branch of electrical engineering and mechanical engineering problems, specifical problems of spring-mass mechanical systems, through the description of the analytical solutions of direct current electrical circuits and spring-mass mechanical systems, applying differential equations. This method stands out among the diverse mathematical treatments used to solve this type of engineering problem, both for its demonstrative elegance and capacity to offer a total answer. Likewise, practical engineering examples related to the studied subject were presented and the results were obtained and analyzed by using the mathematical software, Wolfram Mathematica.

Simulation of the Erosion-Corrosion Destruction Process of Steam Turbine Low-Pressure Cylinder Blades

This paper is devoted to the important problem of creating a method for predicting the intensity of erosion-corrosion destruction of the working blades material of low-pressure stages of powerful steam turbines, considering the complex physical processes that accompany the emergence, transformation of erosion-hazardous droplets and their interaction with elements of the flow path. The features of the construction of some existing erosion models are considered. The model that is developed at the Institute of Mechanical Engineering Problems of the National Academy of Sciences of Ukraine and based on a deterministic-statistical approach to its creation is analyzed in detail. The calculations performed during the research showed a satisfactory coincidence with the results of experimental tests by various authors, obtained on droplet impact stands, which contributed to the successful use of the model in creating a comprehensive method of predicting the wear of working blades. To do this, it was supplemented by the method of determining the parameters of the erosive environment based on the droplet movement equation, taking into account the size distribution law. Along with that, it is shown that the considered model, although it allows to carry out fairly accurate prediction of the development of erosive wear at the modern level, has difficulties in its construction due to the need to identify it from the data of full-scale experiments, the number of which is limited. It has been established that one of the important reasons for the discrepancies that arise is the failure to include of the electrophysical component of the processes occurring in a two-phase flow. In this regard, the results of comprehensive studies of steam electrization and its influence on the working processes of wet steam turbines are considered. It is shown that the change in the properties of the working medium as a functional erosive medium as a result of electrification causes a significant increase (relative to neutral wet steam) of electrochemical processes. At the same time, the kinetics of the damage accumulation to the metal surface layer changes due to the joint occurrence of several negative processes: droplet impact influence; electrochemical processes caused by mechanical and structural and chemical heterogeneity of the surface; hydrogen absorption; changes in mechanical properties under the electric field influence. It was noted that the contribution of hydrogen absorption to changes in mechanical properties is the greatest. According to a preliminary assessment, the complex negative droplet impact and electrophysical influence on the metal surface reduces the incubation period and intensifies the erosion-corrosion process by approximately 2 times

An Effective Hybridization of Quantum-based Avian Navigation and Bonobo Optimizers to Solve Numerical and Mechanical Engineering Problems

An Effective Hybridization of Quantum-based Avian Navigation and Bonobo Optimizers to Solve Numerical and Mechanical Engineering Problems

MFO-SFR: An Enhanced Moth-Flame Optimization Algorithm Using an Effective Stagnation Finding and Replacing Strategy

Moth-flame optimization (MFO) is a prominent problem solver with a simple structure that is widely used to solve different optimization problems. However, MFO and its variants inherently suffer from poor population diversity, leading to premature convergence to local optima and losses in the quality of its solutions. To overcome these limitations, an enhanced moth-flame optimization algorithm named MFO-SFR was developed to solve global optimization problems. The MFO-SFR algorithm introduces an effective stagnation finding and replacing (SFR) strategy to effectively maintain population diversity throughout the optimization process. The SFR strategy can find stagnant solutions using a distance-based technique and replaces them with a selected solution from the archive constructed from the previous solutions. The effectiveness of the proposed MFO-SFR algorithm was extensively assessed in 30 and 50 dimensions using the CEC 2018 benchmark functions, which simulated unimodal, multimodal, hybrid, and composition problems. Then, the obtained results were compared with two sets of competitors. In the first comparative set, the MFO algorithm and its well-known variants, specifically LMFO, WCMFO, CMFO, ODSFMFO, SMFO, and WMFO, were considered. Five state-of-the-art metaheuristic algorithms, including PSO, KH, GWO, CSA, and HOA, were considered in the second comparative set. The results were then statistically analyzed through the Friedman test. Ultimately, the capacity of the proposed algorithm to solve mechanical engineering problems was evaluated with two problems from the latest CEC 2020 test-suite. The experimental results and statistical analysis confirmed that the proposed MFO-SFR algorithm was superior to the MFO variants and state-of-the-art metaheuristic algorithms for solving complex global optimization problems, with 91.38% effectiveness.