Hasil untuk "Descriptive and experimental mechanics"

Tien-Thinh Le, Quoc Dat Ha, Huan Thanh Duong

This study develops a convolutional neural network model to predict the apparent mechanical properties of particulate composite materials based on finite element data. The particulate composite material is considered with random inclusions in size and position. The datasets for training and testing processes are generated by using a validated finite element simulation. Various parametric studies are then investigated, including model efficiency and uncertainty propagation. Moreover, the influence of the constituents and microstructure is numerically revealed based on the proposed convolutional neural network model. It is shown that the developed convolutional neural network model is capable of capturing the microstructural features and provides accurate predictions of apparent mechanical properties of particulate composite materials.

Jing Hu, Lei Guo, Shusheng Zhang

Flow-induced vibration (FIV) characteristics are key factors in enhancing heat transfer. However, challenges such as insufficient heat transfer enhancement and the fatigue strength of the tube bundle persist in the context of improving the heat transfer in elastic tube bundle heat exchangers. This study proposes a novel passive heat transfer enhancement paradigm for elastic tube bundles based on externally induced self-excited oscillations of fluid. By constructing a non-contact energy transfer system, the external oscillation energy is directed into the elastic tube bundle heat exchanger, achieving dynamic stress buffering and breaking through the steady-state flow heat transfer boundary layer. A three-dimensional fluid–structure interaction numerical model is established using Star CCM+2021.3 (16.06.008) to conduct a comparative analysis of the flow characteristics and heat transfer performance between the original structure without an oscillator and the improved structure equipped with a fluid oscillator. The results indicate that the improved structure, through the periodic unsteady jet induced by the fluid oscillator, significantly enhances the turbulence intensity of the shell-side fluid, with the turbulent kinetic energy increasing by over 50%. The radial flow area is notably expanded, thereby reducing the thermal resistance of the boundary layer. At cooling fluid velocities of 6 to 9 m/s, the heat transfer capability of the improved structure is enhanced by more than 50%. Compared with the original structure, the new structure, due to the loading of an external oscillation structure, causes the cold air to present a periodic up and down jet phenomenon. This jet phenomenon, on the one hand, increases the heat exchange area between the cold air and the outer surface of the tube bundle, thereby enhancing the heat exchange capacity. On the other hand, the large-area impact of the fluid reduces the thickness of the boundary layer, lowers the thermal resistance and thereby enhances the heat exchange capacity. Furthermore, this improved structure buffers the mechanical vibrations through self-excited oscillations of the fluid medium, ensuring that the stress levels in the tube bundle remain below the fatigue threshold, effectively mitigating the failure risks associated with traditional active vibration strategies.

Felipe Inostroza-Ríos, Pablo Merino-Muñoz, Celso Sánchez-Ramírez et al.

The running cycle is distinguished from the gait cycle by the presence of a flight phase and distinct biomechanical characteristics. Despite existing frameworks for the temporal segmentation of running, these models remain underutilized in comprehensive biomechanical analyses, particularly for delineating phases, subphases, and key events. This study aims to provide a review of historical and contemporary temporal models of the running cycle and to introduce a unified structure designed to enhance analytical precision. The proposed framework divides the running cycle into two primary phases: (a) contact (subdivided into braking and propulsion subphases) and (b) flight, together with three critical events: (1) initial contact, (2) transition of braking–propulsion, (3) toe-off. While leg swing is not considered a phase in this framework due to temporal overlap with other phases, its recognized importance in running mechanics warrants its integrated analysis under the proposed temporal phase delineation. Additionally, methodologies for identifying these events through dynamometry and motion capture are evaluated, emphasizing their role in contextualizing kinetic and kinematic data. By integrating this temporal structure, the study aims to standardize biomechanical assessments of running technique, fostering more consistent comparisons across studies. Such integration has the potential to not only refine interpretations of running mechanics but also to enable practical advancements in athletic training, injury mitigation, and performance optimization.

Chung-Chi Sun, Rajinder Pal

The effects of polymer concentration on rheology, surface tension, and electrical conductivity of polymer–surfactant mixtures are investigated experimentally. The polymer studied is a cationic quaternary ammonium salt of hydroxyethyl cellulose, and the surfactant used is anionic sodium lauryl sulfate. The polymer concentration is varied from 1000 to 4000 ppm, and the surfactant concentration varied from 0 to 500 ppm. Polymer concentration affects the properties of the mixtures substantially. At a given surfactant concentration, the consistency of the polymer–surfactant mixture rises sharply with the increase in polymer concentration. The mixture also becomes more shear-thinning with the increase in polymer concentration. The surface tension decreases substantially, and the electrical conductivity increases with the increase in polymer concentration at a fixed surfactant concentration. At a given polymer concentration, the consistency index generally exhibits a maximum and the surface tension exhibits a minimum at some intermediate surfactant concentration. With the increase in polymer concentration, the maximum in the consistency index and the minimum in surface tension shift to higher surfactant concentrations. Although the exact mechanisms are not clear at present, a possible explanation for the observed initial changes in rheological and surface-active properties of polymer–surfactant mixtures with the addition of surfactant is charge neutralization and entanglement of polymer chains. At high surfactant concentrations, recharging and disentanglement of polymer chains probably take place.

Yazhou Li, Saizheng Cao, Jiaxing Liao et al.

The nonmagnetic kagome metal ScV6Sn6 displays an unconventional charge order (CO) accompanied by signatures of an anomalous Hall effect, hidden magnetism, and multiple lattice instabilities. In this study, we report the observation of unconventional anomalous thermoelectric properties. Notably, unexpected anomalous transverse Nernst signals reach a peak value of ~4 μV/K near the TCDW ~92 K in ScV6Sn6, and these signals persist in the charge-ordered state as the temperature decreases to 10 K. Furthermore, both thermopower and thermal conductivity exhibit significant changes under magnetic fields, even in the nonmagnetic ground state. These observations strongly suggest the emergence of time-reversal symmetry breaking in ScV6Sn6, as supported by muon spin relaxation (μSR) measurements. While hidden magnetism represents the most plausible origin, alternative mechanisms involving orbital currents and chiral charge order remain possible.

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.

Bruno de Mendonça Braga, Willian Hans Goes Corrêa, Valentin Ferenczi

In the past few decades, much has been done regarding the descriptive set theory of separable Banach spaces. However, the descriptive properties of separable Fréchet spaces have not yet been investigated. In these notes, we look at this problem, its relation with the (now standard) theory for separable Banach spaces, and we compute/estimate the descriptive complexity of some classical classes of separable Fréchet spaces such as Fréchet-Hilbert, Schwartz, nuclear, and Montel spaces. Our main result shows that the class of Montel spaces is complete coanalytic. Noticeably, this applies outside the realm of descriptive set theory and solves an old problem regarding Fréchet spaces satisfying the Heine--Borel property (i.e., Montel spaces). Precisely, we show that there is no separable Montel space containing isomorphic copies of all separable Montel spaces.

Francesco Montecchia, Paola Papoff

High-flow nasal cannula (HFNC) is becoming the gold standard to treat respiratory distress at any age since it potentially provides several significant clinical advantages. An obstacle to the diffusion of this simple and effective system of oxygen therapy is the impossibility to know the optimal flow rate leading to such advantages that allows the reduction in the respiratory effort without causing hyperinflation. To assist clinicians during HFNC treatment in setting the optimal flow rate and in determining the most relevant parameters related to respiratory mechanics and the effort of the patient, we developed a new programmable data monitoring, acquisition, and elaborating system (Pro_HFNC). The application of Pro_HFNC is fully compatible with HFNC as it is interfaced with patient through a facial mask and two specific catheters. The unavoidable and unpredictable loss of air flow occurring around the contour of the mask is evaluated and compensated by a specific algorithm implemented by Pro_HFNC. Our preliminary clinical trials on pediatric patients treated with HFNC show that Pro_HFNC is actually capable to detect for any specific patient both the lower threshold of the delivered flow beyond which the benefits of HFNC application are reached and all the parameters useful for a complete evaluation of the respiratory profile. Pro_HFNC can really help physicians in setting the optimal flow rate during HFNC treatment, thus allowing for the most effective HFNC performance.

Federico Belliardo, Fabio Zoratti, Vittorio Giovannetti

The recent advances in machine learning hold great promise for the fields of quantum sensing and metrology. With the help of reinforcement learning, we can tame the complexity of quantum systems and solve the problem of optimal experimental design. Reinforcement learning is a powerful model-free technique that allows an agent, typically a neural network, to learn the best strategy to reach a certain goal in a completely a priori unknown environment. However, in general, we know something about the quantum system with which the agent is interacting, at least that it follows the rules of quantum mechanics. In quantum metrology, we typically have a model for the system, and only some parameters of the evolution or the initial state are unknown. We present here a general machine learning technique that can optimize the precision of quantum sensors, exploiting the knowledge we have on the system through model-aware reinforcement learning. This framework has been implemented in the Python package qsensoropt, which is able to optimize a broad class of problems found in quantum metrology and quantum parameter estimation. The agent learns an optimal adaptive strategy that, based on previous outcomes, decides the next measurements to perform. We have explored some applications of this technique to NV centers and photonic circuits. So far, we have been able to certify better results than the current state-of-the-art controls for many cases. The machine learning technique developed here can be applied in all scenarios where the quantum system is well-characterized and relatively simple and small. In these cases, we can extract every last bit of information from a quantum sensor by appropriately controlling it with a trained neural network. The qsensoropt software is available on PyPI and can be installed with pip.

Yunpeng Xue, Yongling Zhao, Shuo-Jun Mei et al.

Urban areas are known for their complex atmospheric environments, with the building morphology having a significant impact on local climate patterns, air quality, and overall urban microclimate. Understanding the heat transport and fluid flow in complex urban environments is crucial for improving urban climate resilience, which remains an open frontier in the field of urban studies. To gain a more profound insight into the physical processes occurring in urban areas, particularly within street canyons, we conducted an experimental investigation in a large-scale water tunnel. This study involved the simultaneous examination of heat and flow fields, carried out at high spatial and temporal resolutions, utilizing Laser-induced Fluorescence (LIF) for heat analysis and Particle Image Velocimetry (PIV) for flow analysis. Our results of heat and flow in different street canyons indicate that the flow is significantly influenced by a combination of factors, including canyon configuration, the presence of buoyant force, and the magnitude of the approaching flow. The ventilation rate and heat flux from the street canyon, which are key factors shaping the urban microclimate, are found dominated significantly by the street canyon morphology. For instance, changing the aspect ratio of a street canyon results in a significant change of air ventilation rate, ranging from as low as 0.02 to as high as 1.5 under the same flow conditions. Additionally, canyons with high air ventilation rates exhibit significant heat flux removal at the canyon roof level, which is accurately described by the local Richardson number.

Melih İs

This paper aims to examine the version of the topological group structure in proximity and especially descriptive proximity spaces, that is, the concepts of proximal group and descriptive proximal group are introduced. In addition, the concepts of homomorphism and isomorphism, which give important results in group theory, are discussed by interpreting the concepts of continuity in the theory of (descriptive) proximity.

V. Kireyeu, G. Coci, S. Glaessel et al.

The formation of weakly bound clusters in the hot and dense environment at midrapidity is one of the surprising phenomena observed experimentally in heavy-ion collisions from a low center of mass energy of a few GeV up to a ultra-relativistic energy of several TeV. Three approaches have been advanced to describe the cluster formation: coalescence at kinetic freeze-out, cluster formation during the entire heavy-ion collision by potential interaction between nucleons and deuteron production by hadronic reactions. We identify experimental observables, which can discriminate these production mechanisms for deuterons.

Maria E. Gonzalez Perdomo, Sharifah Wan Madihi

Water-based fracturing fluids are among the most common fluid types used in hydraulic fracturing operations. However, these fluids tend to cause damage in water-sensitive formations. Foam comprises a small amount of base fluid, and compressible gas such as carbon dioxide and nitrogen has emerged as a more ecologically friendly option to fracture such formations. Foam is an attractive option since it has a low density and high viscosity. The applicability of foamed frac fluid is characterized by foam stability and rheology, encompassing the viscosity and proppant carrying ability. The foam quality, pressure and temperature affect the foam rheology. Generally, foam viscosity and stability increase with pressure but decrease when the temperature increases. Hence, it is essential to preserve foam stability in high pressure and high temperature (HPHT) reservoir conditions. The addition of nanoparticles could increase the thermal stability of the foam. This article provides the basis of foam-based fracturing fluid characterization for an optimal application in HPHT reservoir conditions. Then, focusing on improving thermal stability, it reviews the research progress on the use of nanoparticles as foam stabilizing agent. This paper also sheds light on the literature gaps that should be addressed by future research.

Volodymer Voliyanuk, Dmitry Mishchuk, Maksym Parkhomenko

Проблема керування дволанковим маніпулятором залежить від рівня складності системи. При використанні дволанкового маніпулятора потрібно вирішити дві проблеми. Перша проблема полягає в тому, що потрібно визначити параметри керування таким чином, щоб задана позиція маніпулятора змінювалась за відповідною траєкторією. Друга проблема полягає в тому, що для досягнення потрібної позиції маніпулятора потрібно правильно параметризувати математичну модель системи управління. Однією з проблем, яка впливає на точність управління маніпуляторами полягає в правильній побудові адекватних динамічних моделей керування. В даному дослідженні запропоновано розглянути прямий метод розробки динамічної моделі маніпулятора. Пропонується застосовувати математичну модель, яка побудована на принципах теоретичної механіки із застосування рівнянь Лагранжа другого роду. Для створення динамічної моделі дволанкового маніпулятора було визначено кінетичну та потенціальні енергії складових систем маніпулятора, а на основі рівняння Лагранжа другого роду отримано динамічні рівняння руху. Також було визначено залежності між координатами захоплювача та узагальненими координатами. Ці залежності допомогли встановити рівняння управління, які дозволяють здійснити керування за характеристиками руху захоплювача дволанкового маніпулятора.

Maxwell J. D. Ramstead, Dalton A. R. Sakthivadivel, Conor Heins et al.

The aim of this paper is to introduce a field of study that has emerged over the last decade called Bayesian mechanics. Bayesian mechanics is a probabilistic mechanics, comprising tools that enable us to model systems endowed with a particular partition (i.e., into particles), where the internal states (or the trajectories of internal states) of a particular system encode the parameters of beliefs about external states (or their trajectories). These tools allow us to write down mechanical theories for systems that look as if they are estimating posterior probability distributions over the causes of their sensory states. This provides a formal language for modelling the constraints, forces, potentials, and other quantities determining the dynamics of such systems, especially as they entail dynamics on a space of beliefs (i.e., on a statistical manifold). Here, we will review the state of the art in the literature on the free energy principle, distinguishing between three ways in which Bayesian mechanics has been applied to particular systems (i.e., path-tracking, mode-tracking, and mode-matching). We go on to examine a duality between the free energy principle and the constrained maximum entropy principle, both of which lie at the heart of Bayesian mechanics, and discuss its implications.

Kamran Ahmed, Waqar A. Khan, Tanvir Akbar et al.

The present investigation aims to examine the heat flux mechanism in the hagnetohydrodynamic (MHD) mixed convective flow of Williamson-type fluid across an exponential stretching porous curved surface. The significant role of thermal conductivity (variable), non-linear thermal radiation, unequal source-sink, and Joules heating is considered. The governing problems are obtained using the Navier–Stokes theory, and the appropriate similarity transformation is applied to write the partial differential equations in the form of single-variable differential equations. The solutions are obtained by using a MATLAB-based built-in bvp4c package. The vital aspect of this analysis is to observe the effects of the curvature parameter, magnetic number, suction/injection parameter, permeability parameter, Prandtl factor, Eckert factor, non-linear radiation parameter, buoyancy parameter, temperature ratio parameter, Williamson fluid parameter, and thermal conductivity (variable) parameter on the velocity field, thermal distribution, and pressure profile which are discussed in detail using a graphical approach. The correlation with the literature reveals a satisfactory improvement in the existing results on permeability factors in Williamson fluids.

Muhammad Hanafi Yusop, Mohd Fairusham Ghazali, Mohd Fadhlan Mohd Yusof et al.

A common issue in water infrastructure is that it suffers from leakage. The hydroinformatics technique for recognizing the presence of leaks in the pipeline system by means of pressure transient analysis was briefly explored in this study. Various studies have been done of improvised leak detection methods, and Hilbert Huang Transform has the potential to overcome the concern. The HHT processing algorithm has been successfully proven through simulation and experimentally tested to evaluate the ability of pressure transient analysis to predict and locate the leakage in the pipeline system. However, HHT relies on the selection of the suitable IMF in the pre-processing phase which will determine the precision of the estimated leak location. This paper introduces a NIKAZ filter technique for automatic selector of Intrinsic Mode Function (IMF). A laboratory-scale experimental test platform was constructed with a 68-metre long Medium Polyethylene (MDPE) pipe with 63 mm in diameter used for this study and equipped with a circular orifice as an artificial leak in varying sizes with a system of 2 bar to 4 bar water pressure. The results showed that, although with a low ratio of signal-to-noise, the proposed method could be used as an automatic selector for Intrinsic Mode Function (IMF). Experimental tests showed the efficiency, and the work method was successful as an automatic selector of IMF. The proposed mathematical algorithm was then finally evaluated on field measurement tested on-site of a real pipeline system. The results recommended NIKAZ as an automatic selector of IMF to increase the degree of automation of HHT technique, subsequently enhancing the detection and identification of water pipeline leakage.

Marvin E. Goldstein

The focus of this paper is on Rapid Distortion Theory on transversely sheared mean flows, which is often used to investigate turbulence-solid surface interactions. The main purpose of the paper is to bring together and present in a consistent fashion a general theory that has been developed in several different papers that have been published in the Journal of Fluid Mechanics. The equations for the unsteady pressure and velocity flections (which decouple from the entropy fluctuations) are rewritten in terms of a gauge function in order to obtain expressions that involve two arbitrarily convected quantities. A pair of very general conservation laws are used to derive upstream boundary conditions that relate these quantities to the actual physical variables. The entropy fluctuations can be determined after the fact once the solutions for the pressure and velocity fluctuations are known. The result involves a third arbitrary convected quantity that is equal to the entropy fluctuations at upstream infinity and can, therefore, be specified as an additional upstream boundary condition. A secondary purpose of the paper is to summarize a number of applications of the theory that have also appeared in the literature and show how they compare with an experiment.

Anh Tay Nguyen, N. D. Anh

The paper presents a new approach to the conventional averaging in which the role of boundary values is considered in a more detailed way. It results in a new weighted local averaging operator (WLAO) taking into account the particular role of boundary values. A remarkable feature of WLAO is that this operator contains a parameter of boundary regulation p and depends on a local value h of the integration domain. By varying these two parameters one can regulate the obtained approximate solutions in order to get more accurate ones. It has been shown that the combination of WLAO with Galerkin method can lead to an effective approximate tool for the buckling problem of columns and for the frequency analysis of free vibration of strongly nonlinear systems. 

Carlo Nonino, Stefano Savino

An in-house finite element method (FEM) procedure is used to carry out a numerical study on the thermal behavior of cross-flow double-layered microchannel heat sinks with an unequal number of microchannels in the two layers. The thermal performance is compared with those yielded by other more conventional flow configurations. It is shown that if properly designed, i.e., with several microchannels in the top layer smaller than that in the bottom layer, cross-flow double-layered microchannel heat sinks can provide an acceptable thermal resistance and a reasonably good temperature uniformity of the heated base with a header design that is much simpler than that required by the counter-flow arrangement.