Hasil untuk "Descriptive and experimental mechanics"

Aryaman Jha, Kurt Wiesenfeld, Jorge Laval

Motivated by earlier numerical evidence for a percolation-like transition in space-time jamming, we present an analytic description of the transient dynamics of the deterministic traffic model elementary cellular automaton rule 184 (ECA184). By exploiting the deterministic structure of the dynamics, we reformulate the problem in terms of a height function constructed directly from the initial condition, and obtain an equilibrium statistical mechanics-like description over the lattice configurations. This formulation allows macroscopic observables in space-time, such as the total jam delay and jam relaxation time, as well as microscopic jam statistics, to be expressed in terms of geometric properties of the height function. We thereby derive the associated scaling forms and recover the critical exponents previously observed in numerical studies. We discuss the physical implications of this space-time geometric approach.

Hang Li, Kivanc Ekici

Presented in this work is a harmonic balance (HB)-based pseudo-time code-coupling approach applied to a one-degree-of-freedom vortex-induced vibration (VIV) problem of a circular cylinder in a low-Reynolds-number laminar flow regime. Unlike physical time coupling used in traditional time-accurate methods, this novel approach updates both of the fluid and structure fields by integrating respective HB forms of governing equations in pseudo-time, and then couples the two fields in pseudo-time using a partitioned approach. A separate procedure is adopted to determine the VIV frequency at every code-coupling iteration, which enables the simultaneous convergence of variables of both fields in a single run of the solver. For the cases considered here, lock-in vibrations are predicted over a range of Reynolds numbers, inside and outside the resonant range. The results are verified by a time-accurate method and also validated against earlier experimental data, demonstrating the efficiency and robustness of the pseudo-time code-coupling approach.

Zhen Chen, Yingqi Li, Xiaoping Xiao et al.

This paper investigates the design methodologies of pure rolling helical gear pumps with various tooth profiles, based on the active design of meshing lines. The transverse active tooth profile of a pure rolling helical gear end face is composed of various function curves at key control points. The entire transverse tooth profile consists of the active tooth profile and the Hermite curve as the tooth root transition, seamlessly connecting at the designated control points. The tooth surface is created by sweeping the entire transverse tooth profile along the pure rolling contact curves. The fundamental design parameters, tooth profile equations, tooth surface equations, and a two-dimensional fluid model for pure rolling helical gears were established. The pressure pulsation characteristics of pure rolling helical gear pumps and CBB-40 involute spur gear pumps, each with different tooth profiles, were compared under specific working pressures. This comparison encompassed the maximum effective positive and negative pressures within the meshing region, pressure fluctuations at the midpoints of both inlet and outlet pressures, and pressure fluctuations at the rear sections of the inlet and outlet pressures. The results indicated that the proposed pure rolling helical gear pump with a parabolic tooth profile exhibited 42.81% lower effective positive pressure in the meshing region compared to the involute spur gear pump, while the maximum effective negative pressure was approximately 27 times smaller than that of the involute gear pump. Specifically, the pressure pulsations in the middle and rear regions of the inlet and outlet pressure zones were reduced by 33.1%, 6.33%, 57.27%, and 69.61%, respectively, compared to the involute spur gear pump.

Haiqi Wu, Kai Xu

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

Hiroyuki Kogawa, Takashi Wakui, Masatoshi Futakawa

Microbubbles have been applied in various fields. In the mercury targets of spallation neutron sources, where cavitation damage is a crucial issue for life estimation, microbubbles are injected into the mercury to absorb the thermal expansion of the mercury caused by the pulsed proton beam injection and reduce the macroscopic pressure waves, which results in reducing the damage. Recently, when the proton beam power was increased and the number of injected gas bubbles was increased, unique damage morphologies were observed on the solid–liquid interface. Detailed observation and numerical analyses revealed that the microscopic pressure emitted from the gas bubbles contracting is sufficient to form pit damage, i.e., the directions of streak-like defects which are formed by connecting the pit damage coincides with the direction of the gas bubble trajectories, and the distances between the pits was understandable when taking the natural period of gas bubble vibration into account. This indicates that gas microbubbles, used to reduce macroscopic pressure waves, have the potential to be inceptions of cavitation damage due to the microscopic pressure emitted from these gas bubbles. To completely mitigate the damage, we have to consider the two effects of injecting gas bubbles: reducing macroscopic pressure waves and reducing the microscopic pressure due to bubble dynamics.

Jorge Lerendegui-Marco, Víctor Babiano-Suárez, Javier Balibrea-Correa et al.

Abstract This work presents GN-Vision, a novel dual γ-ray and neutron imaging system, which aims at simultaneously obtaining information about the spatial origin of γ-ray and neutron sources. The proposed device is based on two position sensitive detection planes and exploits the Compton imaging technique for the imaging of γ-rays. In addition, spatial distributions of slow- and thermal-neutron sources (<100 eV) are reconstructed by using a passive neutron pin-hole collimator attached to the first detection plane. The proposed gamma-neutron imaging device could be of prime interest for nuclear safety and security applications. The two main advantages of this imaging system are its high efficiency and portability, making it well suited for nuclear applications were compactness and real-time imaging is important. This work presents the working principle and conceptual design of the GN-Vision system and explores, on the basis of Monte Carlo simulations, its simultaneous γ-ray and neutron detection and imaging capabilities for a realistic scenario where a 252Cf source is hidden in a neutron moderating container.

Xue Qi Koh, Calvin Thenarianto, Ville Jokinen et al.

Abstract Controlling the wetting and spreading of microdroplets is key to technologies such as microfluidics, ink‐jet printing, and surface coating. Contact angle goniometry is commonly used to characterize surface wetting by droplets, but the technique is ill‐suited for high contact angles close to 180 °. Here, we attach a micrometric‐sized droplet to an atomic force microscope cantilever to directly quantify droplet–solid friction on different surfaces (superhydrophobic and underwater superoleophobic) with sub‐nanonewton force resolutions. We demonstrate the versatility of our approach by performing friction measurements using different liquids (water and oil droplets) and under different ambient environments (in air and underwater). Finally, we show that underwater superoleophobic surfaces can be qualitatively different from superhydrophobic surfaces: droplet–solid friction is highly sensitive to droplet speeds for the former but not for the latter surface.

Naoya Ozawa, Keisuke Nakamura, Yasuhiro Sakemi

Abstract We have developed a Yb-doped fiber amplifier (YDFA) using fusion splicing, and characterized its performance with numerical simulation, achieving a continuous output of above 10 W $10~{\mathrm{W}}$ of 1064 nm $1064~{\mathrm{nm}}$ light. The device has been conceptualized to have a configuration as simple as possible; a strategy for using fiber amplifiers in harsh environments where quick repairs and replacements may become necessary at unexpected times.

Diwakar Khadka, Satish Adhikari, Atit Pokharel et al.

Solar energy utilization in places like Nepal, is often obstructed by unpredicted environmental factors and existing technological barriers. The challenges encountered often result in fluctuating energy outputs, hindering the transition to greener energy solutions. To tackle these issues, this study introduces a custom-designed Maximum Power Point Tracking (MPPT) controller, seamlessly incorporated into a microcontroller-based battery charging system. This approach seeks to enhance the efficiency of photovoltaic (PV) systems, aligning with the global shift towards renewables. The research's primary objective is to enhance PV module power yield employing MPPT techniques, thereby reducing dependency on non-renewable energy sources. Key goals include real-time MPP tracking for optimal power extraction from PV modules and the integration of a real-time monitoring mechanism for PV and battery states. Leveraging a coordinated interplay of sensors measuring temperature, voltage, and current, vital metrics are fed to the microcontroller. This, in turn, generates a precise Pulse Width Modulation (PWM) signal, fine-tuning the voltage regulation of the buck-boost converter Metal Oxide Semiconductor Field Effect Transistor (MOSFET) for optimal operation. The adopted approach emphasizes monitoring environmental metrics, overseeing power outputs, and generating PWM signals to adeptly manage the buck-boost converter MOSFET voltage. Concurrently, data is transmitted hourly to a cloud platform, facilitating real-time monitoring capabilities showcasing the IoT application. As a result of these integrations, an efficiency improvement of approximately 37.28% was observed. In essence, this research underscores the profound impact of merging advanced technologies within the renewable energy sector, offering a robust blueprint for enhancing energy stability and productivity.

M. F. P. Wagner, K.-O. Voss, C. Trautmann et al.

Abstract The existing and future accelerator facilities at GSI and FAIR offer unique opportunities for interdisciplinary research, especially for material science and nanotechnology. On their way through polymers, swift heavy ions with GeV energy deposit enormous energy densities along their trajectory, generating long nanoscopic damage trails known as ion tracks. Ion-track technology utilizes the small track size (few nm) combined with the extensive track length (up to 100 μm and more) to synthesize and control the geometry of high-aspect-ratio nanostructures such as tailored nanochannels and nanowires. In particular, electrodeposition and ion-track nanotechnology provide an excellent platform for developing unique 3D networks of nanowires with controlled dimensions, composition and crystallographic properties. Here, a summary of recent results obtained on the synthesis and characterization of stable 3D architectures of semiconductor and semimetal nanowires, and their implementation in the fields of photoelectrochemistry and thermoelectrics, is presented.

Elena Kustova, Mariia Mekhonoshina, Anna Bechina et al.

Bulk viscosity and acoustic wave propagation in polyatomic gases and their mixtures are studied in the frame of one-temperature and multi-temperature continuum models developed using the generalized Chapman–Enskog method. Governing equations and constitutive relations for both models are written, and the dispersion equations are derived. In the vibrationally nonequilibrium multi-component gas mixture, wave attenuation mechanisms include viscosity, thermal conductivity, bulk viscosity, diffusion, thermal diffusion, and vibrational relaxation; in the proposed approach these mechanisms are fully coupled contrarily to commonly used models based on the separation of classical Stokes–Kirchhoff attenuation and relaxation. Contributions of rotational and vibrational modes to the bulk viscosity coefficient are evaluated. In the one-temperature approach, artificial separation of rotational and vibrational modes causes great overestimation of bulk viscosity whereas using the effective internal energy relaxation time yields good agreement with experimental data and molecular-dynamic simulations. In the multi-temperature approach, the bulk viscosity is specified only by rotational modes. The developed two-temperature model provides excellent agreement of theoretical and experimental attenuation coefficients in polyatomic gases; both the location and the value of its maximum are predicted correctly. One-temperature dispersion relations do not reproduce the non-monotonic behavior of the attenuation coefficient; large bulk viscosity improves its accuracy only in the very limited frequency range. It is emphasized that implementing large bulk viscosity in the one-temperature Navier–Stokes–Fourier equations may lead to unphysical results.

Xabier Oianguren-Asua, Albert Solé, Carlos F. Destefani et al.

In this chapter we offer an introduction to weak values from a three-fold perspective: first, outlining the protocols that enable their experimental determination; next, deriving their correlates in the quantum formalism and, finally, discussing their ontological significance according to different quantum theories or interpretations. We argue that weak values have predictive power and provide novel ways to characterise quantum systems. We show that this holds true regardless of ongoing ontological disputes. And, still, we contend that certain "hidden" variables theories like Bohmian mechanics constitute very valuable heuristic tools for identifying informative weak values or functions thereof. To illustrate these points, we present a case study concerning quantum thermalization. We show that certain weak values, singled out by Bohmian mechanics as physically relevant, play a crucial role in elucidating the thermalization time of certain systems, whereas standard expectation values are "blind" to the onset of thermalization.

Indrani Bose

A natural phenomenon occurring in a living system is an outcome of the dynamics of the specific biological network underlying the phenomenon. The collective dynamics have both deterministic and stochastic components. The stochastic nature of the key processes like gene expression and cell differentiation give rise to fluctuations (noise) in the levels of the biomolecules and this combined with nonlinear interactions give rise to a number of emergent phenomena. In this review, we describe and discuss some of these phenomena which have the character of phase transitions in physical systems. We specifically focus on noise-induced transitions in a stochastic model of gene expression and in a population genetics model which have no analogs when the dynamics are solely deterministic in nature. Some of these transitions exhibit critical-point phenomena belonging to the mean-field Ising universality class of equilibrium phase transitions. A number of other examples, ranging from biofilms to homeostasis in adult tissues, are also discussed which exhibit behavior similar to critical phenomena in equilibrium and nonequilbrium phase transitions. The examples illustrate how the subject of statistical mechanics provides a bridge between theoretical models and experimental observations.

Huy Dao, Dung D. Le, Cuong Chu

State-of-the-art methods on conversational recommender systems (CRS) leverage external knowledge to enhance both items' and contextual words' representations to achieve high quality recommendations and responses generation. However, the representations of the items and words are usually modeled in two separated semantic spaces, which leads to misalignment issue between them. Consequently, this will cause the CRS to only achieve a sub-optimal ranking performance, especially when there is a lack of sufficient information from the user's input. To address limitations of previous works, we propose a new CRS framework KLEVER, which jointly models items and their associated contextual words in the same semantic space. Particularly, we construct an item descriptive graph from the rich items' textual features, such as item description and categories. Based on the constructed descriptive graph, KLEVER jointly learns the embeddings of the words and items, towards enhancing both recommender and dialog generation modules. Extensive experiments on benchmarking CRS dataset demonstrate that KLEVER achieves superior performance, especially when the information from the users' responses is lacking.

Atta Muhammad, Rajat Srivastava, Nikos Koutroumanis et al.

The development of nanocomposites relies on structure-property relations, which necessitate multiscale modeling approaches. This study presents a modelling framework that exploits mesoscopic models to predict the thermal and mechanical properties of nanocomposites starting from their molecular structure. In detail, mesoscopic models of polypropylene (PP) and graphene based nanofillers (Graphene (Gr), Graphene Oxide (GO), and reduced Graphene Oxide (rGO)) are considered. The newly developed mesoscopic model for the PP/Gr nanocomposite provides mechanistic information on the thermal and mechanical properties at the filler-matrix interface, which can be then exploited to enhance the prediction accuracy of traditional continuum simulations by calibrating the thermal and mechanical properties of the filler-matrix interface. Once validated through a dedicated experimental campaign, this multiscale model demonstrates that with the modest addition of nanofillers (up to 2 wt.%), the Young's modulus and thermal conductivity show up to 35% and 25% enhancement, respectively, while the Poisson's ratio slightly decreases. Among the different combinations tested, PP/Gr nanocomposite shows the best mechanical properties, whereas PP/rGO demonstrates the best thermal conductivity. This validated mesoscopic model can contribute to the development of smart materials with enhanced mechanical and thermal properties based on polypropylene, especially for mechanical, energy storage, and sensing applications.

Alessandra Borrelli, Giulia Giantesio, Maria Cristina Patria

In this paper, we describe our study of the mixed convection of a Boussinesquian Bingham fluid in a vertical channel in the absence and presence of an external uniform magnetic field normal to the walls. The velocity, the induced magnetic field, and the temperature are analytically obtained. A detailed analysis is conducted to determine the plug regions in relation to the values of the Bingham number, the buoyancy parameter, and the Hartmann number. In particular, the velocity decreases as the Bingham number increases. Detailed considerations are drawn for the occurrence of the reverse flow phenomenon. Moreover, a selected set of diagrams illustrating the influence of various parameters involved in the problem is presented and discussed.

Berislav Buča, Katja Klobas, Tomaž Prosen

We review recent results on an exactly solvable model of nonequilibrium statistical mechanics, specifically the classical Rule 54 reversible cellular automaton and some of its quantum extensions. We discuss the exact microscopic description of nonequilibrium dynamics as well as the equilibrium and nonequilibrium stationary states. This allows us to obtain a rigorous handle on the corresponding emergent hydrodynamic description, which is treated as well. Specifically, we focus on two different paradigms of Rule 54 dynamics. Firstly, we consider a finite chain driven by stochastic boundaries, where we provide exact matrix product descriptions of the nonequilibrium steady state, most relevant decay modes, as well as the eigenvector of the tilted Markov chain yielding exact large deviations for a broad class of local and extensive observables. Secondly, we treat the explicit dynamics of macro-states on an infinite lattice and discuss exact closed form results for dynamical structure factor, multi-time-correlation functions and inhomogeneous quenches. Remarkably, these results prove that the model, despite its simplicity, behaves like a regular fluid with coexistence of ballistic (sound) and diffusive (heat) transport. Finally, we briefly discuss quantum interpretation of Rule 54 dynamics and explicit results on dynamical spreading of local operators and operator entanglement.

Haoyu Wang, Hongming Zhang, Muhao Chen et al.

Event mentions in text correspond to real-world events of varying degrees of granularity. The task of subevent detection aims to resolve this granularity issue, recognizing the membership of multi-granular events in event complexes. Since knowing the span of descriptive contexts of event complexes helps infer the membership of events, we propose the task of event-based text segmentation (EventSeg) as an auxiliary task to improve the learning for subevent detection. To bridge the two tasks together, we propose an approach to learning and enforcing constraints that capture dependencies between subevent detection and EventSeg prediction, as well as guiding the model to make globally consistent inference. Specifically, we adopt Rectifier Networks for constraint learning and then convert the learned constraints to a regularization term in the loss function of the neural model. Experimental results show that the proposed method outperforms baseline methods by 2.3% and 2.5% on benchmark datasets for subevent detection, HiEve and IC, respectively, while achieving a decent performance on EventSeg prediction.

Ling Wu, Ludovic Noels

Artificial Neural Networks (NNWs) are appealing functions to substitute high dimensional and non-linear history-dependent problems in computational mechanics since they offer the possibility to drastically reduce the computational time. This feature has recently been exploited in the context of multi-scale simulations, in which the NNWs serve as surrogate model of micro-scale finite element resolutions. Nevertheless, in the literature, mainly the macro-stress-macro-strain response of the meso-scale boundary value problem was considered and the micro-structure information could not be recovered in a so-called localization step. In this work, we develop Recurrent Neural Networks (RNNs) as surrogates of the RVE response while being able to recover the evolution of the local micro-structure state variables for complex loading scenarios. The main difficulty is the high dimensionality of the RNNs output which consists in the internal state variable distribution in the micro-structure. We thus propose and compare several surrogate models based on a dimensionality reduction: i) direct RNN modeling with implicit NNW dimensionality reduction, ii) RNN with PCA dimensionality reduction, and iii) RNN with PCA dimensionality reduction and dimensionality break down, i.e. the use of several RNNs instead of a single one. Besides, we optimize the sequential training strategy of the latter surrogate for GPU usage in order to speed up the process. Finally, through RNN modeling of the principal components coefficients, the connection between the physical state variables and the hidden variables of the RNN is revealed, and exploited in order to select the hyper-parameters of the RNN-based surrogate models in their design stage.

Guilherme Eduardo Freire Oliveira

As an important subject in non-equilibrium Statistical Mechanics, we study in this thesis the relaxation to equilibrium of a photon gas in contact with an non-relativistic and non-degenerate electron bath. Photons and electrons interact via the Compton effect, establishing thermal equilibrium of radiation with matter as pointed out by A.S. Kompaneets in 1957. The evolution of the photon distribution function is then described by the eponymous partial differential equation, here viewed as the diffusion approximation to the relativistic Boltzmann equation that describes the system. Being one of the few examples where this diffusion approximation can be performed in great detail, yielding the Bose-Einstein distribution as stationary solution, the Kompaneets equation also provides the description of the so-called Sunyaev-Zeldovich effect, which is the change of apparent brightness of the cosmic microwave background (CMB) radiation. There are many ways of deriving this equation, but one of them, which was proposed by Kompaneets in 1957 stands out for its directness and simplicity. However, we point out in this work that there are some inconsistencies regarding this traditional derivation that were repeated by all the references we could find that follow the original framework of 1957. This thesis is divided in two parts: in the first we will be interested in how to deal with these inconsistencies, building the necessary basis in which the diffusion approximation to the Boltzmann equation is consistently performed. In the second part, we will be interested in possible extensions and beyond reviewing some existing extensions, we will also show that a new setup involving a master equation of a random walk with suitable chosen transition rates in the photon reciprocal space furnishes not only Kompaneets equation but also a first generalization to a system of bosons under a possible driving.