Hasil untuk "Mechanics of engineering. Applied mechanics"

Gergely Molnár, Julien Réthoré

The study tackles the challenge of accurately modeling fracture behavior in beam lattices, which is essential for designing robust architected materials. Our research focuses on evaluating how the lattice's microstructure and material properties affect fracture toughness. We employed finite element simulations based on the Euler-Bernoulli beam theory to investigate crack propagation, using a failure criterion that initiates beam fracture when maximum axial stress exceeds critical strength. Building on observations from these simulations, we developed a multi-phase-field fracture model with Cosserat elasticity to integrate consistent toughness characteristics into a comprehensive framework for lattice design. This model was validated through experimental tests, ensuring a close match between theoretical predictions and physical reality. Our findings reveal that the energy release rate remains relatively stable during crack propagation, underscoring its reliability as a measure of the toughness of periodic lattice structures. We discovered that toughness is predominantly influenced by beam height and material properties such as tensile strength and Young's modulus, while slenderness has minimal impact. Additionally, cracks were observed to preferentially propagate along the lattice's structural directions due to stress localization effects, highlighting the importance of the microstructure in fracture behavior. The implications of this research are significant, suggesting that improved modeling of fracture in lattice structures can enhance material design reliability and optimization. This study bridges the gap between theoretical models and real-world applications, providing valuable insights for the development of advanced materials with tailored fracture properties.

Karina Kohl, Luigi Carro

Software Engineering (SE) faces simultaneous pressure from AI automation (reducing code production costs) and hardware-energy constraints (amplifying failure costs). We position that SE must redefine itself around human discernment-intent articulation, architectural control, and verification-rather than code construction. This shift introduces accountability collapse as a central risk and requires fundamental changes to research priorities, educational curricula, and industrial practices. We argue that Software Engineering, as traditionally defined around code construction and process management, is no longer sufficient. Instead, the discipline must be redefined around intent articulation, architectural control, and systematic verification. This redefinition shifts Software Engineering from a production-oriented field to one centered on human judgment under automation, with profound implications for research, practice, and education.

Saeid Sahmani, Kamila Kotrasova, Muhammad Atif Shahzad et al.

In the ongoing research examination, a meshfree-based numerical curvature sensitive framework is advanced to analyze the nonlinear asymmetric thermomechanical stability characteristics of microsize curved beams composed of functionally graded materials (FGMs) and subjected to an arbitrary-located concentrated load, uniform temperature rise as well as diverse end supports. As a means to apprehend size dependencies, the nonlocal couple stress theory (NCST) continuum elasticity theory is executed contingent the fifth-order shear flexible curved beam formulations incorporating the thickness stretch. Therefore, as a pioneer exploration, the size-dependent curvature sensitive model of concentrated loaded microsize curved beam is mathematically formulated. To originate the numerical curvature sensitive model, the radial point interpolation meshfree technique is utilized embracing the variation of the nodal points density based upon the background decomposition method (BDM). It is realized that the temperature rise causes to elevate the concentrated loads attributed to the upper limit points, while it leads to decline the concentrated loads associated with the lower limit points. Also, by combination of the softening consequence related to the nonlocal stress tensor with high temperature rise, the number of detected limit points allied to the small curvature sensitivity parameter increases from two points to four points.

Xuyang Sun, Wenchang Tan, Yi Man

The acoustofluidic method holds great promise for manipulating microorganisms. When exposed to the steady vortex structures of acoustic streaming flow, these microorganisms exhibit intriguing dynamic behaviors, such as hydrodynamic trapping and aggregation. To uncover the mechanisms behind these behaviors, we investigate the swimming dynamics of both passive and active particles within a two-dimensional acoustic streaming flow. By employing a theoretically calculated streaming flow field, we demonstrate the existence of stable bounded orbits for particles. Additionally, we introduce rotational diffusion and examine the distribution of particles under varying flow strengths. Our findings reveal that active particles can laterally migrate across streamlines and become trapped in stable bounded orbits closer to the vortex center, whereas passive particles are confined to movement along the streamlines. We emphasize the influence of the flow field on the distribution and trapping of active particles, identifying a flow configuration that maximizes their aggregation. These insights contribute to the manipulation of microswimmers and the development of innovative biological microfluidic chips.

Ashis Kumar Mandal, Md Nadim, Chanchal K. Roy et al.

Research in software engineering is essential for improving development practices, leading to reliable and secure software. Leveraging the principles of quantum physics, quantum computing has emerged as a new computational paradigm that offers significant advantages over classical computing. As quantum computing progresses rapidly, its potential applications across various fields are becoming apparent. In software engineering, many tasks involve complex computations where quantum computers can greatly speed up the development process, leading to faster and more efficient solutions. With the growing use of quantum-based applications in different fields, quantum software engineering (QSE) has emerged as a discipline focused on designing, developing, and optimizing quantum software for diverse applications. This paper aims to review the role of quantum computing in software engineering research and the latest developments in QSE. To our knowledge, this is the first comprehensive review on this topic. We begin by introducing quantum computing, exploring its fundamental concepts, and discussing its potential applications in software engineering. We also examine various QSE techniques that expedite software development. Finally, we discuss the opportunities and challenges in quantum-driven software engineering and QSE. Our study reveals that quantum machine learning (QML) and quantum optimization have substantial potential to address classical software engineering tasks, though this area is still limited. Current QSE tools and techniques lack robustness and maturity, indicating a need for more focus. One of the main challenges is that quantum computing has yet to reach its full potential.

Ella Koresh, Ronit D. Gross, Yuval Meir et al.

Convolutional neural networks (CNNs) evaluate short-range correlations in input images which progress along the layers, whereas vision transformer (ViT) architectures evaluate long-range correlations, using repeated transformer encoders composed of fully connected layers. Both are designed to solve complex classification tasks but from different perspectives. This study demonstrates that CNNs and ViT architectures stem from a unified underlying learning mechanism, which quantitatively measures the single-nodal performance (SNP) of each node in feedforward (FF) and multi-head attention (MHA) sub-blocks. Each node identifies small clusters of possible output labels, with additional noise represented as labels outside these clusters. These features are progressively sharpened along the transformer encoders, enhancing the signal-to-noise ratio. This unified underlying learning mechanism leads to two main findings. First, it enables an efficient applied nodal diagonal connection (ANDC) pruning technique without affecting the accuracy. Second, based on the SNP, spontaneous symmetry breaking occurs among the MHA heads, such that each head focuses its attention on a subset of labels through cooperation among its SNPs. Consequently, each head becomes an expert in recognizing its designated labels, representing a quantitative MHA modus vivendi mechanism. This statistical mechanics inspired viewpoint enables to reveal macroscopic behavior of the entire network from the microscopic performance of each node. These results are based on a compact convolutional transformer architecture trained on the CIFAR-100 and Flowers-102 datasets and call for their extension to other architectures and applications, such as natural language processing.

Premika S. Thasu, Gaurav Kumar, Subrahmanyam Duvvuri

Recent experimental studies reveal that the near-wake region of a circular cylinder at hypersonic Mach numbers exhibits self-sustained flow oscillations. The oscillation frequency was found to have a universal behavior. Experimental observations suggest an aeroacoustic feedback loop to be the driving mechanism of oscillations. An analytical aeroacoustic model which predicts the experimentally observed frequencies and explains the universal behavior is presented here. The model provides physical insights and informs of flow regimes where deviations from universal behavior are to be expected.

Jiaming Liu, Chunying Chen, Yuliang Zhao

Graphdiyne is a new member of the family of carbon‐based nanomaterials that possess two types of carbon atoms, sp‐ and sp2‐hybridized carbon atoms. As a novel 2D carbon‐based nanomaterial with unique planar structure, such as uniformly distributed nanopores and large conjugated structure, graphdiyne has shown many fascinating properties in mechanics, electronics, and optics since it was first experimentally synthesized in 2010. Up to now, graphdiyne and its derivatives have been reported to be successfully applied in many areas, such as catalysis, energy, environment, and biomedicine, due to these excellent properties. Herein, the current research progress of graphdiyne‐based materials in biomedical fields is summarized, including biosensing, biological protection, cancer therapy, tissue engineering, etc. The advantages of graphdiyne and its derivatives are presented and compared with other carbon‐based materials. Considering the potential biomedical and clinical applications of graphdiyne‐based materials, the toxicity and biocompatibility are also discussed based on current studies. Finally, future perspectives and possible biomedical applications of graphdiyne‐based materials are also discussed.

Zhenghu Zhang, Ke Ma, Hua Li et al.

Chatzisavvas I., Arsenyev I., Grahnert R.

In this work, an coupled end-to-end approach for the optimization of the rotor dynamic behavior of a dual-spool aircraft engine along with fatigue life optimization of squirrel cages (SQC) is presented. A realistic model to simulate the rotor dynamics is created, where the high-pressure (HP) rotor is supported by two squirrel cages. The aim of this work is to find a robust rotor dynamics design by shifting a critical speed to higher rotational speed, and at the same time improving the squirrel cage design with respect to fatigue life. Fully automatized coupled analysis process chain is implemented, allowing to compute the influence of the SQCs geometry variation onto the full rotor dynamics and structural performance of the SQC. Two global optimization techniques are employed to explore the SQCs design space and find optimal 3D geometries, using the aforementioned coupled process. Optimization results are compared and discussed in detail, indicating the importance of the numerical optimization to improve fatigue life of the squirrel cage. It is shown that optimized and non-optimized SQC designs, both fulfilling rotor dynamics goals, can have significantly different performance regarding their fatigue life. Moreover, the advantage of the coupled process is illustrated, allowing to find superior SQC designs by considering both disciplines simultaneously in comparison with a sequential (uncoupled) approach, when the target elastic properties of an SQC, selected only based on the rotor dynamics requirements, may lead to sub-optimal fatigue life.

Ho Vinh Nguyen, Vo Duy Cong, Trung Phan Xuan

This paper develops a computer vision system integrated with a SCARA robot arm to pick and place objects. A novel method to calculate the 3D coordinates of the objects from a camera is proposed. This method helps simplify the camera calibration process. It requires no knowledge of camera modeling and mathematical knowledge of coordinate transformations. The least square method will predate the Equation describing the relationship between pixel coordinates and 3D coordinates. An image processing algorithm is presented to detect objects by color or pixel intensity (thresholding method). The pixel coordinates of the objects are then converted to 3D coordinates. The inverse kinematic Equation is applied to find the joint angles of the SCARA robot. A palletizing application is implemented to test the accuracy of the proposed method. The kinematic Equation of the robot arm is presented to convert the 3D position of the objects to the robot joint angles. So, the robot moves exactly to the required positions by providing suitable rotational movements for each robot joint. The experiment results show that the robot can pick and place 27 boxes on the conveyor to the pallet with an average time of 2.8s per box. The positions of the boxes were determined with an average error of 0.5112mm and 0.6838mm in the X and Y directions, respectively.

Sura F. Yousif

This article presents a performance comparison of two known public key cryptography techniques namely RSA (Rivest–Shamir–Adleman) and El-Gamal algorithms to encrypt/decrypt the speech signals during transferring over open networks. Specifically, this work is divided into two stages. The first stage is enciphering-deciphering the input speech file by employing the RSA method. The second stage is enciphering-deciphering the same input speech file by employing the El-Gamal method. Then, a comparative analysis is performed to test the performance of both cryptosystems using diverse experimental and statistical analyses for the ciphering and deciphering procedures like some known speech quality measures: histogram, spectrogram, correlation, differential, speed performance and noise effect analyses. The analyses outcomes reveal that the RSA and El-Gamal approaches are efficient and adequate for providing high degree of security, confidentiality and reliability. Additionally, the outcomes indicate that the RSA speech cryptosystem outperforms its counterpart the El-Gamal speech cryptosystem in most of ciphering/deciphering speech performance metrics.

Jia Sun, Yinghua Liu, Yizheng Wang et al.

We proposed the boundary-integral type neural networks (BINN) for the boundary value problems in computational mechanics. The boundary integral equations are employed to transfer all the unknowns to the boundary, then the unknowns are approximated using neural networks and solved through a training process. The loss function is chosen as the residuals of the boundary integral equations. Regularization techniques are adopted to efficiently evaluate the weakly singular and Cauchy principle integrals in boundary integral equations. Potential problems and elastostatic problems are mainly concerned in this article as a demonstration. The proposed method has several outstanding advantages: First, the dimensions of the original problem are reduced by one, thus the freedoms are greatly reduced. Second, the proposed method does not require any extra treatment to introduce the boundary conditions, since they are naturally considered through the boundary integral equations. Therefore, the method is suitable for complex geometries. Third, BINN is suitable for problems on the infinite or semi-infinite domains. Moreover, BINN can easily handle heterogeneous problems with a single neural network without domain decomposition.

Rahul Deshpande, Aman G. Kidanemariam, Ivan Marusic

The present study tests the efficacy of the well-known viscous drag reduction strategy of imposing spanwise wall oscillations to reduce pressure drag contributions in a transitional- and fully-rough turbulent wall flow. This is achieved by conducting a series of direct numerical simulations of a turbulent flow over two-dimensional (spanwise aligned) semi-cylindrical rods, placed periodically along the streamwise direction with varying streamwise spacing. Surface oscillations, imposed at fixed viscous-scaled actuation parameters optimum for smooth wall drag reduction, are found to yield substantial drag reduction (>25%) for all the rough wall cases, maintained at matched roughness Reynolds numbers. While the total drag reduction is due to a drop in both viscous and pressure drag in the case of transitionally-rough flow (i.e. with large inter-rod spacing), it is solely associated with pressure drag reduction for the fully-rough cases (i.e. with small inter-rod spacings), with the latter being reported for the first time. The study finds that pressure drag reduction in all cases is caused by the attenuation of the vortex shedding activity in the roughness wake, in response to wall-oscillation frequencies that are of the same order as the vortex shedding frequencies. Contrary to speculations in the literature, this study confirms that the mechanism behind pressure drag reduction, achieved via imposition of spanwise oscillations, is independent from the viscous drag reduction. This mechanism is responsible for weakening of the Reynolds stresses and increase in base pressure in the roughness wake, explaining the pressure drag reduction observed by past studies, across varying roughness heights and geometries.

E. Qian, Ionut-Gabriel Farcas, K. Willcox

We present a new scientific machine learning method that learns from data a computationally inexpensive surrogate model for predicting the evolution of a system governed by a time-dependent nonlinear partial differential equation (PDE), an enabling technology for many computational algorithms used in engineering settings. Our formulation generalizes to the function space PDE setting the Operator Inference method previously developed in [B. Peherstorfer and K. Willcox, Data-driven operator inference for non-intrusive projection-based model reduction, Computer Methods in Applied Mechanics and Engineering, 306 (2016)] for systems governed by ordinary differential equations. The method brings together two main elements. First, ideas from projection-based model reduction are used to explicitly parametrize the learned model by low-dimensional polynomial operators which reflect the known form of the governing PDE. Second, supervised machine learning tools are used to infer from data the reduced operators of this physics-informed parametrization. For systems whose governing PDEs contain more general (non-polynomial) nonlinearities, the learned model performance can be improved through the use of lifting variable transformations, which expose polynomial structure in the PDE. The proposed method is demonstrated on two examples: a heat equation model problem that demonstrates the benefits of the function space formulation in terms of consistency with the underlying continuous truth, and a three-dimensional combustion simulation with over 18 million degrees of freedom, for which the learned reduced models achieve accurate predictions with a dimension reduction of five orders of magnitude and model runtime reduction of up to nine orders of magnitude.

G. Walton