Hasil untuk "Mechanics of engineering. Applied mechanics"

Marcin Fronc, Marek Borowiec, Grzegorz Litak et al.

Nonlinear energy harvesting systems based on multibody structures constitute a promising solution for autonomous devices powered by ambient vibrations. This paper presents the modeling and control of a nonlinear energy harvester employing a double pendulum configuration and BLDC motors operating as generators. The primary objective of the study was to develop a control strategy that enables the maximization of harvested power while simultaneously improving the energy conversion efficiency during the charging of the battery supplying the target system. The developed model incorporates the mechanical equations of motion of the double pendulum, an electrical model of the BLDC motors, and two independently controlled buck–boost converters, each connected to one joint of the pendulum. In addition, a perturb-and-observe (P&O) maximum power point tracking (MPPT) algorithm was implemented, which utilizes a portion of the computational resources of the target system’s microcontroller and allows for dynamic adjustment of the electrical loads seen by the generators. Simulation results obtained in the Simulink environment confirm that the application of independent power converters combined with local MPPT control leads to an increase in the total harvested power and ensures more stable battery charging under conditions of variable mechanical excitation. The obtained results demonstrate the effectiveness of the proposed approach and indicate its potential applicability in self-powered systems operating in environments characterized by irregular and stochastic vibrations.

Le Hoai Phuong, Phan Xuan Trung, Phan Trong Quyen et al.

This paper presents an automated pick-and-place robotic system utilizing stereo vision technology for object detection and localization in 3D space. Stereo vision is an optimal choice for short-range industrial applications due to its capability of providing accurate depth measurements at a reasonable cost, outperforming alternatives such as LiDAR or Time-of-Flight (ToF) cameras in similar settings. The proposed system is designed to operate reliably under natural lighting conditions, making it well-suited for deployment in factory production lines. An Intel RealSense D435 camera is employed to capture both RGB and depth images from the environment. Object detection is performed using a YOLOv11-based model, achieving high detection accuracy with a mean average precision (mAP50) of 98.5% across all object classes. The system processes depth information to identify the topmost object, estimates its 3D coordinates with minimal errors (average positional errors below 5.3 mm), and transmits the data to a robotic manipulator for execution of the pick-and-place task. Experimental results demonstrate the system's high precision and reliability in object detection and 3D localization.

Rupesh Shah, Ajay `Bhanubhai Makwana

Conducting experimental studies with prototypes is both costly and time-intensive. Scaled-down models can be used for initial experiments. However, scaling requires high precision and clarity. The present study aims to evaluate and compare various scaling approaches for predicting prototype results and to relate the temperature distribution of the scaled model to that of the prototype. Steady-state simulations were carried out using the k–ε turbulence closure model and the probability density function approach. Temperature profiles and stream traces from different scaling methods were compared to identify the optimum scale-down method. Simulation results showed a change in the position of the combustion core; the radial temperature profiles for scale-down models with 50% heat input showed a significant deviation of about 85% at an axial position of 0.1 m, while for the rest of the axial positions, the difference was less than 7%. The results reveal that geometrically larger models align more closely with experimental data, particularly when using the Constant Residence Time (CRT) method, compared to smaller-scale models. To account for the dependency of temperature on energy input and energy release at specific locations within the combustor, a non-dimensional temperature variable, θ was introduced. A comparison of results indicates that the CRT method effectively scales down the combustor, showing strong agreement with experimental data from the literature. The variation in θ for scaled-down models aligns closely with prototype and experimental results, with maximum deviations of 17%. This suggests that θ is a novel and effective variable for establishing reliable connections between scaled-down models and prototypes.

Luca Cocconi, Paolo Malgaretti, Holger Stark

Isothermal information engines operate by extracting net work from a single heat bath through measurement and feedback control. In this work, we analyze a realistic active Szilard engine operating on a single active particle by means of steric interaction with an externally controlled mechanical element. In particular, we provide a comprehensive study of how finite measurement accuracy affects the engine's work and power output, as well as the cost of operation. Having established the existence of non-trivial optima for work and power output, we study the dependence of their loci on the measurement error parameters and identify conditions for their positivity under one-shot and cyclic engine operation. By computing a suitably defined information efficiency, we also demonstrate that this engine design allows for the violation of Landauer's bound on the efficiency of information-to-work conversion. Notably, the information efficiency for one-shot operation exhibits a discontinuous transition and a non-monotonic dependence on the measurement precision. Finally, we show that cyclic operation improves information efficiency by harvesting residual mutual information between successive measurements.

Mokhtar Mokhtar, Bambang Sugiarto, Askar Adika Agama et al.

In this paper, an investigation of the use of gasoline-ethanol-methanol on the spark ignition engine is presented, it is not common practice on public roads to use three fuels simultaneously in a spark-ignition engine. Using methanol reduces the ignition delay during combustion, especially at lean air-fuel ratios, and reduces knocking potential in small amounts. The best result ignition delay with value λ= 1,3 obtained in the E5M15 mixture with SoC occurred at 325 CAo, while the value λ= 1,0 also obtained on the same mixture with SoC occurred at 321,5 CAo. The CCV results indicate a more sloping increase in the COV (coefficient of variation) value when using GEM fuel, particularly with the addition of more methanol. The addition of methanol enhances combustion progression and improves the ability of the fuel blend to sustain combustion under lean conditions. Regarding the torque and power values, at λ= 1,0; 1,1; 1,2 are not significantly different, while the value λ= 1,3 is below the other λ values.

Shiqiang Li, Yuwei Li, Xiaomin Ma et al.

Abstract The current research of sandwich structures under dynamic loading mainly focus on the response characteristic of structure. The micro-topology of core layers would sufficiently influence the property of sandwich structure. However, the micro deformation and topology mechanism of structural deformation and energy absorption are unclear. In this paper, based on the bi-directional evolutionary structural optimization method and periodic base cell (PBC) technology, a topology optimization frame work is proposed to optimize the core layer of sandwich beams. The objective of the present optimization problem is to maximize shear stiffness of PBC with a volume constraint. The effects of the volume fraction, filter radius, and initial PBC aspect ratio on the micro-topology of the core were discussed. The dynamic response process, core compression, and energy absorption capacity of the sandwich beams under blast impact loading were analyzed by the finite element method. The results demonstrated that the over-pressure action stage was coupled with the core compression stage. Under the same loading and mass per unit area, the sandwich beam with a 20% volume fraction core layer had the best blast resistance. The filter radius has a slight effect on the shear stiffness and blast resistances of the sandwich beams. But increasing the filter radius could slightly improve the bending stiffness. Upon changing the initial PBC aspect ratio, there are three ways for PBC evolution: The first is to change the angle between the adjacent bars, the second is to further form holes in the bars, and the third is to combine the first two ways. However, not all three ways can improve the energy absorption capacity of the structure. Changing the aspect ratio of the PBC arbitrarily may lead to worse results. More studies are necessary for further detailed optimization. This research proposes a new topology sandwich beam structure by micro-topology optimization, which has sufficient shear stiffness. The micro mechanism of structural energy absorption is clarified, it is significant for structural energy absorption design.

Jian Chang, Shuze Zhu

Strain and defect engineering have profound applications in two‐dimensional materials, where it is important to determine the equilibrated atomistic structures with defect conditions under mechanical deformations for computational materials design. Nevertheless, how to efficiently predict relaxed atomistic structures and the associated physical fields on each atom or bond under evolving mechanical deformations remains as an essential challenge. To address this issue, a deep neural network architecture is designed to embed the state of applied strains into the defect‐engineered atomistic geometry, so that deformation‐coupled physical fields of interests on atoms or bonds can be predicted or derived over continuous state of deformations. For demonstration, the combination of applied tensile strains and shear strain on monolayer graphene with random distribution of Stone–Wales defects and vacancy defects is considered. The unique advantage of this framework is the development of strain‐embedding concept combined with generative adversarial network, which can be feasibly extended to other material and other conditions. The computational approach sheds light on boosting the efficiency of evaluating physical properties of 2D materials under complex strain and defect states.

Henrik Ebel, Jan van Delden, Timo Lüddecke et al.

Data-based methods have gained increasing importance in engineering, especially but not only driven by successes with deep artificial neural networks. Success stories are prevalent, e.g., in areas such as data-driven modeling, control and automation, as well as surrogate modeling for accelerated simulation. Beyond engineering, generative and large-language models are increasingly helping with tasks that, previously, were solely associated with creative human processes. Thus, it seems timely to seek artificial-intelligence-support for engineering design tasks to automate, help with, or accelerate purpose-built designs of engineering systems, e.g., in mechanics and dynamics, where design so far requires a lot of specialized knowledge. However, research-wise, compared to established, predominantly first-principles-based methods, the datasets used for training, validation, and test become an almost inherent part of the overall methodology. Thus, data publishing becomes just as important in (data-driven) engineering science as appropriate descriptions of conventional methodology in publications in the past. This article analyzes the value and challenges of data publishing in mechanics and dynamics, in particular regarding engineering design tasks, showing that the latter raise also challenges and considerations not typical in fields where data-driven methods have been booming originally. Possible ways to deal with these challenges are discussed and a set of examples from across different design problems shows how data publishing can be put into practice. The analysis, discussions, and examples are based on the research experience made in a priority program of the German research foundation focusing on research on artificially intelligent design assistants in mechanics and dynamics.

Allan N. Kaufman, Bruce I. Cohen, Alain J. Brizard

Presented here is a transcription of the lecture notes from Professor Allan N. Kaufman's graduate statistical mechanics course at Berkeley from the 1972-1973 academic year. Part 1 addresses equilibrium statistical mechanics with topics: fundamentals, classical fluids and other systems, chemical equilibrium, and long-range interactions. Part 2 addresses non-equilibrium statistical mechanics with topics: fundamentals, Brownian motion, Liouville and Klimontovich equations, Landau equation, Markov processes and Fokker-Planck equation, linear response and transport theory, and an introduction to non-equilibrium quantum statistical mechanics.

Diego C. P. Blanco, Ardeshir Hanifi, Dan S. Henningson et al.

Large-eddy simulations of a flat-plate boundary layer, without a leading edge, subject to multiple levels of incoming free stream turbulence are considered in the present work. Within an input-output model where non-linear terms of the incompressible Navier-Stokes equations are treated as an external forcing, we manage to separate inputs related to perturbations coming through the intake of the numerical domain, whose evolution represents a linear mechanism, and the volumetric non-linear forcing due to triadic interactions. With these, we perform the full reconstruction of the statistics of the flow, as measured in the simulations, to quantify pairs of wavenumbers and frequencies more affected by either linear or non-linear receptivity mechanisms. Inside the boundary layer, different wavenumbers at near-zero frequency reveal streaky structures. Those that are amplified predominantly via linear interactions with the incoming vorticity occur upstream and display transient growth, while those generated by the non-linear forcing are the most energetic and appear in more downstream positions. The latter feature vortices growing proportionally to the laminar boundary layer thickness, along with a velocity profile that agrees with the optimal amplification obtained by linear transient growth theory. The numerical approach presented is general and could potentially be extended to any simulation for which receptivity to incoming perturbations needs to be assessed.

M. Chwała, K. Phoon, M. Uzielli et al.

ABSTRACT This paper is motivated by the Time Capsule Project (TCP) of the International Society for Soil Mechanics and Geotechnical Engineering (ISSMGE). The historical developments of geotechnical risk and reliability are reviewed for the past six decades. The key features distinguishing geotechnical and structural engineering are the natural origin of the ground and the lack of sufficient data to characterize the ground using the more familiar frequentist interpretation of probability. For the first feature, random field theory is applied to model spatial variability and the random finite element method or other methods are proposed for solving soil-structure interaction problems in spatially variable soil. For the second feature, compilation of databases is essential to serve as priors for Bayesian updating and more recently for Bayesian machine learning. There is a gradual evolution towards reliability-based design because probabilistic methods offer a pathway to address big data and implement data-centric geotechnics as one step towards digital transformation. Given the complexity of the natural ground (known unknowns can be large and there are unknown unknowns), engineering judgment remains important to bridge the gap between theory and practice. However, the role of engineering judgment needs to be updated as modern machine learning methods become more powerful.

Dimitri Rossana, Rinaldi Martina, Tornabene Francesco et al.

In a context where daily and seasonal temperature changes or potential fire exposure can affect the mechanical response of structures strengthened with fiber-reinforced polymer (FRP) composites during their life cycle, the present work studies the bond behavior of FRP laminates glued to concrete substrates under a thermal variation. The problem is tackled computationally by means of a contact algorithm capable of handling both the normal and tangential cohesive responses, accounting for the effect of thermal variations on the interfacial strength and softening parameters, which defines the failure surface and post cracking response of the selected specimen. A parametric investigation is performed systematically to check for the effect of thermo-mechanical adhesive and geometrical properties on the debonding load of the FRP-to-concrete structural system. The computational results are successfully validated against some theoretical predictions from literature, which could serve as potential benchmarks for developing further thermo-mechanical adhesive models, even in a coupled sense, for other reinforcement-to-substrate systems, useful for design purposes in many engineering applications.

Shuguang Li, M. Ijaz Khan, Faris Alzahrani et al.

Objective here is to analyze unsteady flow of viscous liquid subject to an induced magnetic field. The flow is controlled through uniform suction. Thermal equation is deliberated with dissipation, Ohmic heating, radiation and entropy rate are discussed in thermodynamical system. Binary chemical reaction is also addressed. Here our prime focus is to scrutinize the thermal transformation and entropy production analyses. Nonlinear differential systems are obtained through suitable transformations. Resulting systems are then numerically solved by finite difference technique. Influence of thermal field induced magnetic field, entropy rate, concentration and velocity against pertinent variables are addressed. An improvement in suction variable leads to augments induced magnetic field. Reverse impact for velocity and thermal fields is observed with magnetic variable. Computational outcomes of temperature gradient and solutal transport rate are examined. Larger magnetic Prandtl number rises induced magnetic and thermal fields. Higher approximation of radiation intensifies the entropy rate. Higher approximation of suction variable decays the thermal field and concentration. High entropy rate is found against magnetic Prandtl number. Larger radiation corresponds to amplifies the temperature.

K. Espoir N'souglo, Katarzyna Kowalczyk-Gajewska, Mohammad Marvi-Mashhadi et al.

In this paper, we have investigated, using finite element calculations, the effect of initial texture on the formation of multiple necking patterns in ductile metallic rings subjected to rapid radial expansion. The mechanical behavior of the material has been modeled with the elasto-viscoplastic single crystal constitutive model developed by \citet{marin2006}. The polycrystalline microstructure of the ring has been generated using random Voronoi seeds. Both $5000$ grain and $15000$ grain aggregates have been investigated, and for each polycrystalline aggregate three different spatial distributions of grains have been considered. The calculations have been performed within a wide range of strain rates varying from $1.66 \cdot 10^4 ~ \text{s}^{-1}$ to $3.33 \cdot 10^5 ~ \text{s}^{-1}$, and the rings have been modeled with four different initial textures: isotropic texture, $\left\langle 001\right\rangle\parallelΘ$ Goss texture, $\left\langle 001\right\rangle\parallel$ R Goss texture and $\left\langle 111\right\rangle\parallel$ Z fiber texture. The finite element results show that: (i) the spatial distribution of grains affects the location of the necks, (ii) the decrease of the grain size delays the formation of the necking pattern and increases the number of necks, (iii) the initial texture affects the number of necks, the location of the necks, and the necking time, (iv) the development of the necks is accompanied by a local increase of the slip activity. This work provides new insights into the effect of crystallographic microstructure on dynamic plastic localization and guidelines to tailor the initial texture in order to delay dynamic necking formation and, thus, to improve the energy absorption capacity of ductile metallic materials at high strain rates.

D. Yulianti, S. Sugianto, K. M. Ngafidin

This century demands that everyone has 21st-century skills. The Covid-19 pandemic era has an impact on education, however, to face the global era, 21st-century skills must still develop in higher education including 21st-century learning skills called 4C skills (Creative, Critical, Collaboration, Communication). The survey of results on students participating in the Mechanics I course shows that creative and critical thinking skills are in a low category, collaboration and communication skills are also in the low category. This study aims to develop 4C skills in the pandemic era through learning Physics in Mechanics course with a Science Technology Engineering and Mathematics (STEM) approach, assisted by Scratch, and to know students’ responses to the applied learning. The research subjects were students in the third semester, who took the Mechanics I course as many as 110 people and were divided into three groups. The research method is a quasi-experiment one-group pretest-posttest design. The research instrument consists of essay tests to measure creative and critical thinking skills and observation sheets to measure collaboration and communication skills. The results of data analysis demonstrated that students' 4C skills increased, the average is in the medium category.