Hasil untuk "Mechanics of engineering. Applied mechanics"

Marlone Vernet, Eric Falcon

At scales larger than the forcing scale, some out-of-equilibrium turbulent systems (such as hydrodynamic turbulence, wave turbulence, and nonlinear optics) exhibit a state of statistical equilibrium where energy is equipartitioned among large-scale modes, in line with the Rayleigh-Jeans spectrum. Key open questions now pertain to either the emergence, decay, collapse, or other nonstationary evolutions from this state. Here, we experimentally investigate the free decay of large-scale hydroelastic turbulent waves, initially in a regime of statistical equilibrium. Using space- and time-resolved measurements, we show that the total energy of these large-scale tensional waves decays as a power law in time. We derive an energy decay law from the theoretical initial equilibrium spectrum and the linear viscous damping, as no net energy flux is carried. Our prediction then shows a good agreement with experimental data over nearly two decades in time, for various initial effective temperatures of the statistical equilibrium state. We further identify the dissipation mechanism and confirm it experimentally. Our approach could be applied to other decaying turbulence systems, with the large scales initially in statistical equilibrium.

Alok Kumar Mohanty

The popularity of electric vehicles has been increasing day by day due to the effect of the pollution caused by the fossil fuels. In the present scenario the difficulty arising in the use of the electric based transportation is the unavailability of the charging stations. So wireless charging system have emerged as one of the solutions for charging of electricity based vehicles. Wireless based electric vehicles come with an opportunity of charging those vehicles that can have the priority of not having charging plugs. Wireless charging technology comes with an option of spark free operation, reliable and user friendly as compared to the plug charging option. This technology makes use of a common charger that can be used for all types of electric operated vehicles. Wireless power transfer technology requires design of inductive coils which involves proper selection of inductances values. A high frequency full bridge converter has to be tuned for wireless power transfer between the transmitter and receiver coils. This paper presents an effective way of charging electric vehicles that has been implemented in Matlab/Simulink simulations. Rigorous simulations were carried out and analysis were conducted to evaluate the system's performance.

Haiqi Wu, Kai Xu

This paper investigates the relationship between categorical entropy and von Neumann entropy of quantum lattices. We begin by studying the von Neumann entropy, proving that the average von Neumann entropy per site converges to the logarithm of an algebraic integer in the low-temperature and thermodynamic limits. Next, we turn to categorical entropy. Given an endofunctor of a saturated A-infinity-category, we construct a corresponding lattice model, through which the categorical entropy can be understood in terms of the information encoded in the model. Finally, by introducing a gauged lattice framework, we unify these two notions of entropy. This unification leads naturally to a sufficient condition for a conjectural algebraicity property of categorical entropy, suggesting a deeper structural connection between A-infinity-categories and statistical mechanics.

Paweł Ziółkowski, Łukasz Witanowski, Stanisław Głuch et al.

This article focuses principally on the comparison baseline and the optimized flow efficiency of the final stage of an axial turbine operating on a gas–steam mixture by applying a hybrid Nelder–Mead and the particle swarm optimization method. Optimization algorithms are combined with CFD calculations to determine the flowpaths and thermodynamic parameters. The working fluid in this study is a mixture of steam and gas produced in a wet combustion chamber, therefore the new turbine type is currently undergoing theoretical research. The purpose of this work is to redesign and examine the last stage of the gas–steam turbine’s flow characteristics. Among the optimized variables, there are parameters characterizing the shape of the endwall contours within the rotor domain. The values of the maximized objective function, which is the isentropic efficiency of the turbine stage, are found from the 3D RANS computation of the flowpath geometry changing during the improvement scheme. The optimization process allows the stage efficiency to be increased by almost 4 percentage points. To achieve high-quality results, a mesh of over 20 million elements is used, where the percentage error in efficiency between the previous and current mesh sizes drops below 0.05%.

Lvyang Yang, Jiankang Zhang, Huaiqiang Li et al.

The digitization of engineering drawings is crucial for efficient reuse, distribution, and archiving. Existing computer vision approaches for digitizing engineering drawings typically assume the input drawings have high quality. However, in reality, engineering drawings are often blurred and distorted due to improper scanning, storage, and transmission, which may jeopardize the effectiveness of existing approaches. This paper focuses on restoring and recognizing low-quality engineering drawings, where an end-to-end framework is proposed to improve the quality of the drawings and identify the graphical symbols on them. The framework uses K-means clustering to classify different engineering drawing patches into simple and complex texture patches based on their gray level co-occurrence matrix statistics. Computer vision operations and a modified Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) model are then used to improve the quality of the two types of patches, respectively. A modified Faster Region-based Convolutional Neural Network (Faster R-CNN) model is used to recognize the quality-enhanced graphical symbols. Additionally, a multi-stage task-driven collaborative learning strategy is proposed to train the modified ESRGAN and Faster R-CNN models to improve the resolution of engineering drawings in the direction that facilitates graphical symbol recognition, rather than human visual perception. A synthetic data generation method is also proposed to construct quality-degraded samples for training the framework. Experiments on real-world electrical diagrams show that the proposed framework achieves an accuracy of 98.98% and a recall of 99.33%, demonstrating its superiority over previous approaches. Moreover, the framework is integrated into a widely-used power system software application to showcase its practicality.

Musil Miloš, Úradníček Juraj, Páleník Marek et al.

The article describes a diagnostic method, on the basis of which it is possible to assess whether there is a loose connection in the clamping system (detection) and to determine which of them is loose (localization). The diagnostic method is based on comparing the drops in the natural frequencies of a measured system with respect to a reference system, which simulates the desired state. When analysing the drop in natural frequencies in order to locate the loosening, it is necessary to relate this drop to the position of the clamping joints within the considered mode shape.

Zhijian Wang, Jennifer Boothby, Devin J. Roach et al.

Sarah Spiekermann, Till Winkler

Digital ethics is being discussed worldwide as a necessity to create more reliable IT systems. This discussion, fueled by the fear of uncontrollable artificial intelligence (AI) has moved many institutions and scientists to demand a value-based system engineering. This article presents how organizations can build responsible and ethically founded systems with the 'Value-based Engineering' (VBE) approach that was standardized in the IEEE 7000TM standard. VBE is a transparent, clearly-structured, step-by-step methodology combining innovation management, risk management, system and software engineering in one process framework. It embeds a robust value ontology and terminology. It has been tested in various case studies. This article introduces readers to the most important steps and contributions of the approach.

Suryani Dyah Astuti, Mohammad H. Tamimi, Anak A.S. Pradhana et al.

Microbes such as Escherichia coli (E. coli) can easily contaminate raw chicken meat in clean conditions, causing decay and unpleasant scents. This study aims to characterize gas patterns by comparing fresh chicken meat and E. coli bacteria contaminated chicken meat based on shelf life using a Gas Sensor Array (GSA) system (MQ2, MQ3, MQ7, MQ8, MQ135, and MQ136) on electronic nose. The findings revealed GSA capability to detect a variety of typical gas patterns formed by the samples. This gas detection property is indicated by the appearance of the variance in the sensors output voltage pattern for each sample variation. The data for fresh and contaminated samples were classified by the random forest (RF) classifier with 99.25% and 98.42% precision, respectively. Furthermore, the support vector machine (SVM) classifier correctly identified the fresh and contaminated samples with 98.61% and 86.66% accuracy, respectively. This finding offers insight for GSA capability in classifying chicken meat contaminated with E. coli using an RF and SVM.

Alexey Semenov

The paper presents an approach to the stress-strain and buckling analysis in fiberglass cylindrical and conical panels reinforced from the concave side with an orthogonal grid of stiffeners. A mathematical model of the Timoshenko (Mindlin–Reissner) type is used. Transverse shears and geometric nonlinearity are taken into account. The stiffeners are introduced in two ways: using the method of refined discrete introduction and the method of structural anisotropy. We use a computational algorithm based on the Ritz method and the best parameter continuation method. We also provide buckling load values and make a comparison between two types of approaches to account for stiffeners, which shows good convergence.

Takeyoshi Nagai, Daisuke Hasegawa, Eisuke Tsutsumi et al.

Persistent and intense mixing hotspots are generated where the Kuroshio flows over steep seamounts, with an increase in energy dissipation by two to three orders of magnitude, according to tow-yo microstructure measurements combined with numerical simulations.

Juri Di Rocco, Davide Di Ruscio, Claudio Di Sipio et al.

To perform their daily tasks, developers intensively make use of existing resources by consulting open-source software (OSS) repositories. Such platforms contain rich data sources, e.g., code snippets, documentation, and user discussions, that can be useful for supporting development activities. Over the last decades, several techniques and tools have been promoted to provide developers with innovative features, aiming to bring in improvements in terms of development effort, cost savings, and productivity. In the context of the EU H2020 CROSSMINER project, a set of recommendation systems has been conceived to assist software programmers in different phases of the development process. The systems provide developers with various artifacts, such as third-party libraries, documentation about how to use the APIs being adopted, or relevant API function calls. To develop such recommendations, various technical choices have been made to overcome issues related to several aspects including the lack of baselines, limited data availability, decisions about the performance measures, and evaluation approaches. This paper is an experience report to present the knowledge pertinent to the set of recommendation systems developed through the CROSSMINER project. We explain in detail the challenges we had to deal with, together with the related lessons learned when developing and evaluating these systems. Our aim is to provide the research community with concrete takeaway messages that are expected to be useful for those who want to develop or customize their own recommendation systems. The reported experiences can facilitate interesting discussions and research work, which in the end contribute to the advancement of recommendation systems applied to solve different issues in Software Engineering.

Danyllo Valente da Silva, Bruno Pedraça de Souza, Taisa Guidini Gonçalves et al.

Contemporary software systems (CSS), such as the internet of things (IoT) based software systems, incorporate new concerns and characteristics inherent to the network, software, hardware, context awareness, interoperability, and others, compared to conventional software systems. In this sense, requirements engineering (RE) plays a fundamental role in ensuring these software systems' correct development looking for the business and end-user needs. Several software technologies supporting RE are available in the literature, but many do not cover all CSS specificities, notably those based on IoT. This research article presents RETIoT (Requirements Engineering Technology for the Internet of Things based software systems), aiming to provide methodological, technical, and tooling support to produce IoT software system requirements document. It is composed of an IoT scenario description technique, a checklist to verify IoT scenarios, construction processes, and templates for IoT software systems. A feasibility study was carried out in IoT system projects to observe its templates and identify improvement opportunities. The results indicate the feasibility of RETIoT templates' when used to capture IoT characteristics. However, further experimental studies represent research opportunities, strengthen confidence in its elements (construction process, techniques, and templates), and capture end-user perception.

Dinulović Mirko, Rašuo Boško, Trninić Marta R. et al.

Honeycomb core composite plates are becoming more important in the construction of primary aerospace structures. Nowadays, these types of materials are used for construction of fuselage skins, central and outer wing boxes, engine tail cones, landing gear doors, command surfaces like spoilers and ailerons etc. To determine the stress strain field in loaded honeycomb plates elastic coefficients are required. In the present work, a method for determining all required elastic coefficients for the core and plates is presented. Using experimentally obtained values for Nomex paper (type 410) and phenolic resin material model is presented and FEA model of composite plate with honeycomb core is created and three point bend test is simulated. Numerically obtained stress and strain values are compared to the experiment. Good agreement between proposed material model and experimentally obtained values is observed.

Alessandra Iacobucci, Stefano Olla, Gabriel Stoltz

We study the macroscopic profiles of temperature and angular momentum in the stationary state of chains of rotors under a thermo-mechanical forcing applied at the boundaries. These profiles are solutions of a system of diffusive partial differential equations with boundary conditions determined by the thermo-mechanical forcing. Instead of expensive Monte Carlo simulations of the underlying microscopic dynamics, we perform extensive numerical computations based on a finite difference method for the system of partial differential equations describing the macroscopic steady state. We first present a formal derivation of these stationary equations based on a linear response argument and local equilibrium assumptions. We then study various properties of the solutions to these equations. This allows to characterize the regime of parameters leading to uphill energy diffusion -- a situation in which the energy flows in the direction of the gradient of temperature -- and to identify regions of parameters corresponding to a negative energy conductivity (i.e. a positive linear response of the energy current to a gradient of temperature). The macroscopic equations we derive are consistent with some previous results obtained by numerical simulation of the microscopic physical system, which confirms their validity.

Lingxiao Wang, Yin Jiang, Kai Zhou

We propose a general framework to extract microscopic interactions from raw configurations with deep neural networks. The approach replaces the modeling Hamiltonian by the neural networks, in which the interaction is encoded. It can be trained with data collected from Ab initio computations or experiments. The well-trained neural networks give an accurate estimation of the possibility distribution of the configurations at fixed external parameters. It can be spontaneously extrapolated to detect the phase structures since classical statistical mechanics as prior knowledge here. We apply the approach to a 2D spin system, training at a fixed temperature, and reproducing the phase structure. Scaling the configuration on lattice exhibits the interaction changes with the degree of freedom, which can be naturally applied to the experimental measurements. Our approach bridges the gap between the real configurations and the microscopic dynamics with an autoregressive neural network.

Frederike Ramin, Christoph Matthies, Ralf Teusner

Motivated and competent team members are a vital part of Agile Software development and make or break any project's success. Motivation is fostered by continuous progress and recognition of efforts. These concepts are founding pillars of the Scrum methodology, which focuses on self-organizing teams. The types of contributions Scrum development team members make to a project's progress are not only technical. However, a comprehensive model comprising the varied contributions in modern software engineering teams is not yet established. We propose a model that incorporates contributions of all Scrum roles, explicitly including those which are not directly related to project artifacts. It improves the visibility of performed tasks, acts as a starting point for team retrospection, and serves as a foundation for discussion in the research community.

Yuh-Chung Hu, Ping-Jung Chen, Pei-Zen Chang

The internal temperature is an important index for the prevention and maintenance of a spindle. However, the temperature inside the spindle is undetectable directly because there is no space to embed a temperature sensor, and drilling holes will reduce its mechanical stiffness. Therefore, it is worthwhile understanding the thermal-feature of a spindle. This article presents a methodology to identify the thermal-feature model of an externally driven spindle. The methodology contains self-made hardware of the temperature sensing and wireless transmission module (TSWTM) and software for the system identification (SID); the TSWTM acquires the temperature training data, while the SID identifies the parameters of the thermal-feature model of the spindle. Then the resulting thermal-feature model is written into the firmware of the TSWTM to give it the capability of accurately calculating the internal temperature of the spindle from its surface temperature during the operation, or predicting its temperature at various speeds. The thermal-feature of the externally driven spindle is modeled by a linearly time-invariant state-space model whose parameters are identified by the SID, which integrates the command “n4sid” provided by the System ID Toolbox of MATLAB and the k-fold cross-validation that is common in machine learning. The present SID can effectively strike a balance between the bias and variance of the model, such that both under-fitting and over-fitting can be avoided. The resulting thermal-feature model can not only predict the temperature of the spindle rotating at various speeds but can also calculate the internal temperature of the spindle from its surface temperature. Its validation accuracy is higher than 98.5%. This article illustrates the feasibility of accurately calculating the internal temperature (undetectable directly) of the spindle from its surface temperature (detectable directly).

Ferfecki Petr, Zaoral Filip, Zapoměl Jaroslav

The computational procedure for investigation vibration stability of a flexible rotor consisting of an asymmetric shaft, one disc, and supported by ball bearings is developed in this work. Lagrange equations of the second kind were used for derivation of the motion equation. The vibration response stability of the Jeffcott-like rotor was studied by means of eigenvalues of a transition matrix. Three different methods for approximation of the transition matrix have been investigated. The presented simulations are focused on studying the influence of parametric excitation produced by the shaft asymmetry and self-excitation vibration caused by the shaft material damping. The numerical results proved the applicability of the developed procedure, which has been verified by the direct integration of the motion equation.

Akram Mahdi Abd , Lutfi Y. Zaidan, Mohsin A. AlShammari

Wear phenomenon is considered as a predominant parameter in the forming processes causing the shorten tool life which in turn increases the production costs. In this study, wear depth of tool components for multi-stages of metal shell of spark plug (MSSP) manufacturing was analyzed using Archard's model with FE simulation software. The 3D geometry models were built using SolidWorks software then the models files were exported to Simufact forming software to do the settings of preprocess and FE analysis. The cylinder compression test and ring compression test were executed to get the flow stress and frictional factor respectively. Product parts dimensions, forging loads, effective stress, relative sliding velocity and contact pressure were solved and discussed. Subsequently to verify the analysis acceptance, the actual product parts measurements which obtained by coordinate measuring machine (CMM), were compared with the simulation results. It was found that the deviations of actual MSSP geometry dimensions less than (4%). Also verification is performed to forging loads at each stage which gives a good agreement between actual and FE simulation results. Finally, the wear depth of tool components were calculated for each stage using the FE software. Based on the results of wear depth from simulation and tolerance rages of actual product parts, tool service lives were predicted to find the productivities for each tool component. The results of FE simulation were compared with the CMM measurements for known productivities tool components from actual production line, which gives a good accuracy and acceptable agreement