Hasil untuk "Mechanics of engineering. Applied mechanics"

N. Lamba, P. Bhad, V. Manthena et al.

In order to examine the relationship between temperature change and thermoelastic deformation in the assumed functionally graded materials (FGMs), the present work involves fractional-order mathematical modeling of a thermoelastic thick elliptical annular plate, where the material properties of the plate are considered thermo-sensitive. To determine the precise thermal response of the problem, the density, heat capacity, and thermal conductivity are also graded axially, in addition to the thermo-sensitivity. The temperature distribution of the plate is assumed to be zero at the start and end of the thickness variation, with convective heat exchange boundary conditions in the spatial direction. Kirchhoff's variable transformation method is used to eliminate temperature-dependent nonlinearity in the heat transfer equations. The Laplace transform for the initial condition and a perturbation solution are obtained through an asymptotic series expansion. The heat transfer problem is ultimately solved using the modified Mathieu transform, the Taylor series technique, and its inversion, yielding the required temperature distribution function in the Laplace domain. Using nickel as the metal and titanium carbide as the ceramic, a model of a ceramic-metal-based FGM is constructed for numerical computations. All results are graphically presented, showing the distributions of temperature, displacement, and stress curves along the spatial variables for two different fractional-order parameters by varying the plate’s thickness. This study enables a deeper understanding of the thermo-mechanical responses of FGMs through its novel application to FGMs with elliptical geometry and its innovative modeling approach incorporating thermally sensitive material properties. It opens the door to optimized designs in thermal industries. The understanding and improvement of thermal behavior in advanced materials have been significantly enhanced by the discovery of the influence of fractional parameters in an FGM elliptical plate.

Alexander Korn, Lea Zaruchas, Chetan Arora et al.

Large Language Models, particularly decoder-only generative models such as GPT, are increasingly used to automate Software Engineering tasks. These models are primarily guided through natural language prompts, making prompt engineering a critical factor in system performance and behavior. Despite their growing role in SE research, prompt-related decisions are rarely documented in a systematic or transparent manner, hindering reproducibility and comparability across studies. To address this gap, we conducted a two-phase empirical study. First, we analyzed nearly 300 papers published at the top-3 SE conferences since 2022 to assess how prompt design, testing, and optimization are currently reported. Second, we surveyed 105 program committee members from these conferences to capture their expectations for prompt reporting in LLM-driven research. Based on the findings, we derived a structured guideline that distinguishes essential, desirable, and exceptional reporting elements. Our results reveal significant misalignment between current practices and reviewer expectations, particularly regarding version disclosure, prompt justification, and threats to validity. We present our guideline as a step toward improving transparency, reproducibility, and methodological rigor in LLM-based SE research.

Sainath K, Karuppasamy R, Prabagaran S

The functionally graded materials (FGMs) have been realised to be potential candidates when it comes to high-pressure projects and applications where thermal and mechanical stability is to be ensured in extreme environments. In the research, the drawback of the widely used stainless steel SS316L facing high-stress conditions in the thermal environment will be overcome by the innovation of two new FGMs composed of SS316L and Inconel 625, Ti6Al4V, and Inconel 718. The aim was to conduct the fabrication and testing of a multi-phase FGM with the help of advanced techniques of manufacturing namely additive manufacturing and powder metallurgy, with the strict control of layer thickness of 0.2 mm and contents of its materials (60% SS316L, 20% Inconel 625 or Ti6Al4V, and 20% Inconel 718). Tensile testing, yield testing, fatigue and creep behaviour were performed at temperatures of −20°C and +60°C. The findings indicated that the FGM containing SS316L, Inconel 625, and Inconel 718 proved to be superior to SS316L at every point where its tensile strength is 992 MPa and its yield strength is 602 MPa, also at a temperature of +60 C versus 460 MPa and 186 MPa tensile and yield strengths in SS316L. The advanced fatigue performance and creep resistance were also indicated because of the better qualities of the alloys Inconel. Such results are indicative of gradient composition and layer formation in augmenting thermal and mechanical capabilities. The research ends up with a conclusion that these FGMs can be considered as excellent prospects in terms of the aerospace and power generation industries where strength and thermal endurance are of essence to the next generation of the industry.

Shrishail Basappa Angadi, Santosh Kumar, Madeva Nagaral et al.

The aerospace and automotive engineering industries are seeing a growing need for materials that are both lightweight and very durable. This increased demand has prompted the development of innovative metal matrix composites based on aluminum. The current study aimed at developing and characterization Al7020 metal matrix composites by reinforcing micro boron carbide particles, Al7020/B4C MMCs are fabricated by stir casting method by varying the boron carbide particles in wt.% (0, 2, 4, 6, and 8wt. %). Lastly, the prepared samples were subjected to tensile, compression, hardness, and fracture toughness tests to evaluate the impact of B4C particles on density, mechanical, and microstructural parameters. By incorporating B4C particles into the Al7020 alloy, the experimental results demonstrated that metal matrix composites exhibited enhanced ultimate tensile strength, yield strength, hardness, and compression strength. In addition, the lowest density, highest toughness, and superior micrograph were observed in Al7020/B4C MMCs with 8 wt. % reinforcement of B4C particles with a minor decrease in elongation.

van Dong Nguyen, Duong Minh Duc, Do Trong Hieu

Overhead crane control with time-varying cable length presents significant challenges, particularly in maintaining accurate trolley positioning while minimizing residual payload oscillations induced by lifting and lowering operations. This paper proposes a hybrid control approach combining Active Disturbance Rejection Control (ADRC) with Zero Vibration Derivative (ZVD) input shaping to address these issues. ADRC enhances system robustness against external disturbances and model uncertainties, providing stable tracking performance. However, due to its limitations in completely suppressing residual oscillations, the ZVD input shaper is integrated to reduce payload sway. To further optimize shaping performance under variable rope lengths, an average cable length strategy is employed for parameter tuning. Additionally, a reference trajectory planning scheme is developed to smooth command inputs, reducing sudden impacts, induced oscillations, and improving overall system stability during crane operation.

Tolentino San L., Mírez Jorge

In the present work, the objective is to determine the Mach number and static pressure flow field behavior for off-design planar nozzle geometries with divergent angles of 1.21° (model A1) and 10.85° (model B1). ANSYS-Fluent R16.2 code was employed, and the RANS model and SAS turbulence model were applied to simulate in 2D the viscous flow field for the nozzle pressure ratio range of NPR 2.49 to 8.91. For model A1, in the divergent, the shock train is present, and the lateral pressure loads show fluctuations; in the centerline, the velocity is in the range of Mach 0.849 to 1.405. For model B1, the shock train is not present in the divergent, and the lateral pressure loads show flow separation; in the centerline, it is in the range of Mach 0.849 to 1.991. The flow velocity at the exit of the A1 model nozzle reaches Mach 1.357, which is 52.2% lower with respect to the B1 model, which has Mach 2.066. However, for the supersonic jet in the region of the atmosphere, the A1 model reaches Mach 2.967, which is 14.9% higher than with respect to the B1 model, which has Mach 2.522.

Max Ofsa, Taylan G. Topcu

Systems engineering (SE) is evolving with the availability of generative artificial intelligence (AI) and the demand for a systems-of-systems perspective, formalized under the purview of mission engineering (ME) in the US Department of Defense. Formulating ME problems is challenging because they are open-ended exercises that involve translation of ill-defined problems into well-defined ones that are amenable for engineering development. It remains to be seen to which extent AI could assist problem formulation objectives. To that end, this paper explores the quality and consistency of multi-purpose Large Language Models (LLM) in supporting ME problem formulation tasks, specifically focusing on stakeholder identification. We identify a relevant reference problem, a NASA space mission design challenge, and document ChatGPT-3.5's ability to perform stakeholder identification tasks. We execute multiple parallel attempts and qualitatively evaluate LLM outputs, focusing on both their quality and variability. Our findings portray a nuanced picture. We find that the LLM performs well in identifying human-focused stakeholders but poorly in recognizing external systems and environmental factors, despite explicit efforts to account for these. Additionally, LLMs struggle with preserving the desired level of abstraction and exhibit a tendency to produce solution specific outputs that are inappropriate for problem formulation. More importantly, we document great variability among parallel threads, highlighting that LLM outputs should be used with caution, ideally by adopting a stochastic view of their abilities. Overall, our findings suggest that, while ChatGPT could reduce some expert workload, its lack of consistency and domain understanding may limit its reliability for problem formulation tasks.

J. Collins, C. O. Smith

Marco Autili, Martina De Sanctis, Paola Inverardi et al.

As testified by new regulations like the European AI Act, worries about the human and societal impact of (autonomous) software technologies are becoming of public concern. Human, societal, and environmental values, alongside traditional software quality, are increasingly recognized as essential for sustainability and long-term well-being. Traditionally, systems are engineered taking into account business goals and technology drivers. Considering the growing awareness in the community, in this paper, we argue that engineering of systems should also consider human, societal, and environmental drivers. Then, we identify the macro and technological challenges by focusing on humans and their role while co-existing with digital systems. The first challenge considers humans in a proactive role when interacting with digital systems, i.e., taking initiative in making things happen instead of reacting to events. The second concerns humans having a reactive role in interacting with digital systems, i.e., humans interacting with digital systems as a reaction to events. The third challenge focuses on humans with a passive role, i.e., they experience, enjoy or even suffer the decisions and/or actions of digital systems. The fourth challenge concerns the duality of trust and trustworthiness, with humans playing any role. Building on the new human, societal, and environmental drivers and the macro and technological challenges, we identify a research roadmap of digital systems for humanity. The research roadmap is concretized in a number of research directions organized into four groups: development process, requirements engineering, software architecture and design, and verification and validation.

Niloofar Vardian

This paper investigates the notion of Krylov complexity, a measure of operator growth, within the framework of 1-matrix quantum mechanics (1-MQM). Krylov complexity quantifies how an operator evolves over time by expanding it in a series of nested commutators with the Hamiltonian. We analyze the Lanczos coefficients derived from the correlation function, revealing their linear growth even in this integrable system. This growth suggests a link to chaotic behavior, typically unexpected in integrable systems. Our findings in both ground and thermal states of 1-MQM provide new insights into the nature of complexity in quantum mechanical models and lay the groundwork for further studies in more complex holographic theories.

Sumio Watanabe

Mathematical equivalence between statistical mechanics and machine learning theory has been known since the 20th century, and research based on this equivalence has provided novel methodologies in both theoretical physics and statistical learning theory. It is well known that algebraic approaches in statistical mechanics such as operator algebra enable us to analyze phase transition phenomena mathematically. In this paper, we review and prospect algebraic research in machine learning theory for theoretical physicists who are interested in artificial intelligence. If a learning machine has a hierarchical structure or latent variables, then the random Hamiltonian cannot be expressed by any quadratic perturbation because it has singularities. To study an equilibrium state defined by such a singular random Hamiltonian, algebraic approaches are necessary to derive the asymptotic form of the free energy and the generalization error. We also introduce the most recent advance: the theoretical foundation for the alignment of artificial intelligence is now being constructed based on algebraic learning theory. This paper is devoted to the memory of Professor Huzihiro Araki who is a pioneering founder of algebraic research in both statistical mechanics and quantum field theory.

Atul Madhukar Zope, Raju Kumar Swami, Atul Patil

This study investigates the barriers that the Lean Six Sigma implementation faces during the assembly of electric vehicles. In order to implement lean Six Sigma methodology in electric vehicle assembly processes effectively, it is crucial to identify and analyze the barriers that hinder process improvement. To identify the obstacles and create a conceptual model, a thorough literature review was conducted. Four factors, namely, integration of assembly, inspection, and testing, lack of trained and knowledgeable human resources, external and in-plant battery transportation, and manual assembly and rigid automation, were found to have the potential to affect the lean Six Sigma implementation. Three drivers, namely assembly cost, assembly time, and assembly effort were selected for the study. The model is then tested using the structural equation modeling and the gathered data. The results show a significant relationship between the three drivers and the four barriers of Lean Six Sigma implementation to the electric vehicle assembly.

Józef Korbicz, Andrzej Janczak

The paper focuses on using of artificial neural networks in model-based fault detection and isolation. Modelling of a system both at its normal operation conditions and in faulty states is considered and a comparative study of three different methods of system modelling that use a linear model, neural network nonlinear autoregressive with exogenous input model, and neural network Wiener model is presented. Application of these models is illustrated with an example of approximation of a dependence of the juice steam pressure in the stage two on the juice steam pressures in the stages one and three of a five stage sugar evaporator. Parameters of the linear model are estimated with the recursive pseudo linear regression method, whilst the backpropagation and truncated backpropagation through time algorithms are employed for training the neural network models. All the considered models are derived based on the experimental data recorded at the Lublin Sugar Factory.

MOHD SAIF, Ritik Kumar Rawat

Wire-electric discharge machining offers a number of benefits in comparison to traditional manufacturing processes likewise, no obvious mechanical cutting traces also hard and rigid materials can be processed perfectly in WEDM. Since, aluminum alloys are used in aerospace, shipbuilding, breathing gas cylinders for scuba diving, surgical components and automotive industry for their high-strength-to-weight ratio, accurate shapes and dimensions. Through this method, complicated structures made of aluminum alloy are produced in a single setup with incredibly tight tolerances. The present investigation explores WEDM for AA6061 to optimize different process variables for attaining performance measures in terms of maximum MRR and minimum SR. Taguchi’s L18 OA matrix, S/N ratio, ANOVA and Grey Relational Analysis were employed to optimize SR and MRR. It has been noted from ANOVA that pulse on time and peak current are the most influential aspects for MRR and SR with their contributions of 13.33% and 16.25% respectively. Further, the best possible considered parameters setting has been established by applying GRA for MRR and SR are, pulse on time-50µs, pulse off time-13µs and peak current-4 amp.

Imre Kozák, Frigges Nándori, Tamás Szabó

We have considered a linearly elastic body loaded by tractions inward normal to the instantaneous surface. Due to the increment of the surface element vector there is a contribution to the tangent stiffness matrix referred to as load correction stiffness matrix. The goal of the numerical experiments is to determine the bifurcation point on the fundamental equilibrium path. Linear eigenvalue problems with follower loads are also analysed.

Doron Grossman, Arezki Boudaoud

The elastic response of mechanical, chemical, and biological systems is often modeled using a discrete arrangement of Hookean springs, either representing finite material elements or even the molecular bonds of a system. However, to date, there is no direct derivation of the relation between a general discrete spring network\blu{, with arbitrary geometry,} and it's corresponding elastic continuum. Furthermore, understanding the network's mechanical response requires simulations that may be expensive computationally. Here we report a method to derive the exact elastic continuum model of any discrete network of springs, requiring network geometry and topology only. We identify and calculate the so-called "non-affine" displacements. Explicit comparison of our calculations to simulations of different crystalline and disordered configurations, shows we successfully capture the mechanics even of auxetic materials. Our method is valid for residually stressed systems with non-trivial geometries, and is an essential step in generalizing active stresses on such discrete systems. It is easily generalizable to other discrete models, and opens the possibility of a rational design of elastic systems.

Li-ping Li, Jie Hu, Shucai Li et al.

Hanfeng Zhai, Quan Zhou, Guohui Hu

Utilizing physical information to improve the performance of the conventional neural networks is becoming a promising research direction in scientific computing recently. For multiphase flows, it would require significant computational resources for neural network training due to the large gradients near the interface between the two fluids. Based on the idea of the physics-informed neural networks (PINNs), a modified deep learning framework BubbleNet is proposed to overcome this difficulty in the present study. The deep neural network (DNN) with separate sub-nets is adopted to predict physics fields, with the semi-physics-informed part encoding the continuity equation and the pressure Poisson equation P for supervision and the time discretized normalizer to normalize field data per time step before training. Two bubbly flows, i.e., single bubble flow and multiple bubble flow in a microchannel, are considered to test the algorithm. The conventional computational fluid dynamics software is applied to obtain the training dataset. The traditional DNN and the BubbleNet(s) are utilized to train the neural network and predict the flow fields for the two bubbly flows. Results indicate the BubbleNet frameworks are able to successfully predict the physics fields, and the inclusion of the continuity equation significantly improves the performance of deep NNs. The introduction of the Poisson equation also has slightly positive effects on the prediction results. The results suggest that constructing semi-PINNs by flexibly considering the physical information into neural networks will be helpful in the learning of complex flow problems.

Ahmad K. Omar, Hyeongjoo Row, Stewart A. Mallory et al.

Nonequilibrium phase transitions are routinely observed in both natural and synthetic systems. The ubiquity of these transitions highlights the conspicuous absence of a general theory of phase coexistence that is broadly applicable to both nonequilibrium and equilibrium systems. Here, we present a general mechanical theory for phase separation rooted in ideas explored nearly a half-century ago in the study of inhomogeneous fluids. The core idea is that the mechanical forces within the interface separating two coexisting phases uniquely determine coexistence criteria, regardless of whether a system is in equilibrium or not. We demonstrate the power and utility of this theory by applying it to active Brownian particles, predicting a quantitative phase diagram for motility-induced phase separation in both two and three dimensions. This formulation additionally allows for the prediction of novel interfacial phenomena, such as an increasing interface width while moving deeper into the two-phase region, a uniquely nonequilibrium effect confirmed by computer simulations. The self-consistent determination of bulk phase behavior and interfacial phenomena offered by this mechanical perspective provide a concrete path forward towards a general theory for nonequilibrium phase transitions.

Deheng Kong, Faquan Wu, C. Saroglou et al.

The importance of in-situ rock block characterization has been realized for decades in rock mechanics and engineering, yet how to reliably measure and characterize the geometrical properties of blocks in varied forms of exposures and patterns of jointing is still a challenging task. Using a point cloud model (PCM) of rock exposures generated from remote sensing techniques, we developed a consistent and comprehensive method for rock block characterization that is composed of two different procedures and a block indicator system. A semi-automatic procedure towards the robust extraction of in-situ rock blocks created by the deterministic discontinuity network on rock exposures (PCM-DDN) was developed. A 3D stochastic discrete fracture network (DFN) simulation (PCM-SDS) procedure was built based on the statistically valid representation of the discontinuity network geometry. A multi-dimensional block indicator system, i.e., the block size, shape, orientation, and spatial distribution pattern for systematic and objective block characterization, was then established. The developed method was applied to a synthetic model of cardboard boxes and three different rock engineering scenarios, including a road cut slope from Spain and two open-pit mining slopes from China. Compared with existing empirical methods, the proposed procedures and the block indicator system are dependable and practically feasible, which can help enhance our understanding of block geometry characteristics in related applications.