Physical Applications Research Articles

Numerical analysis of multiple slip effects on CuO /MgO/TiO2-water ternary hybrid nanofluid with thermal and exponential space-based heat source

In contemporary science, the efficiency of heat transfer holds paramount importance across various engineering applications, where nanofluids play a significantly influential role. Therefore the present study focuses on the significance of multiple slip in heat transfer of3-Dnatural convectional ternary hybrid nanofluid flow across a stretching sheet. The study investigates the implications of a temperature-dependent and exponentially space-based heat source, coupled with non-linear thermal radiation effects. Impact of magnetic field is further considered for the present investigation. The three dissimilar nanoparticles (CuO,MgO&TiO2) are dispersed in water as base fluid to improve the heat transfer. The system of partial differential equations is developed under considered assumptions. The Partial differential equations (PDEs) are transmuted into nonlinear coupled dimensionless system of ordinary differential equations (ODEs) by applying similarity transformations. The reduced dimensionless nonlinear equations are numerically solved by employing shooting scheme via bvp4c built-in function MATLAB. The properties of flow controlling parameters via velocity and thermal profiles are elaborated through graphs. The results designated that the x-direction velocity slip declines the velocity. The momentum profile (velocity profile) is declined for greater stretching ratio parameter. Furthermore, it is analyzed that the thermal field is boosted up for larger amounts of temperature base and exponential space-based heat source. It is concluded that heat conduction capacity is higher in ternary hybrid nanofluid than hybrid and mono nanofluids. Additionally, Prandtl number serves to enhance the Nusselt number with augmentation in Thermal radiation parameter. The physical applications of the considered model in solar energy, heat pumps, heat exchangers, air purifiers, the automotive industry, electrical chillers, turbines, nuclear networks, broadcasters, ships, and biotechnology.

A Systematic Review of Physical Modelling Techniques, Developments and Applications in Slope Stability Analyses

AbstractAn extensive evaluation of published articles suggests a lack of research on a systematic literature review relating to physical modelling techniques, developments and application in slope stability studies. However, there is growing interest in using physical model tests in slope stability investigations. The present study systematically reviews the methodologies and applications of physical modelling in slope stability research. The Scopus database was used to identify relevant studies which employed physical model tests in slope stability investigations. A combination of fifteen keywords was used to identify relevant articles. A PRISMA-P method for conducting a systematic review was adopted. Articles were screened and analysed, and extracted data were re-organised using an Excel sheet. Data relating to research objectives, physical model test techniques, instrumentation, scaling laws, numerical modelling, results, and findings were extracted and analysed. The systematic review highlights gaps requiring further studies, particularly in slope reinforcements using vegetation and strength deterioration of reinforcements performance under repeated loading exposures. It is found that scholars have not fully addressed the influence of loss of water on pore water regimes and its impact on stability when vegetation is applied as reinforcements. It is also found that the development of slope materials for soil slopes in physical model tests relies on the artificial development of such materials with minimal consideration of their long-term behaviour. Although other options, such as bio-cementation and desiccation techniques, which simulate the natural environment of the slopes, are neglected in slope material development, the present study recommends that future studies consider such techniques.

A Review on Hybrid Nanofluids: Preparation Methods, Thermo Physical Properties and Applications

A Review on Hybrid Nanofluids: Preparation Methods, Thermo Physical Properties and Applications

Fermion self-energy and damping rate in a hot magnetized plasma

We derive a general expression for the fermion self-energy in a hot magnetized plasma by using the Landau-level representation. In the one-loop approximation, the Dirac structure of the self-energy is characterized by five different functions that depend on the Landau-level index n and the longitudinal momentum pz. We derive general expressions for all five functions and obtain closed-form expressions for their imaginary parts. The latter receive contributions from three types of on shell processes, which are interpreted in terms of Landau-level transitions, accompanied by a single photon (gluon) emission or absorption. By making use of the imaginary parts of the self-energy functions, we also derive the Landau-level dependent fermion damping rates Γn(pz) and study them numerically in a wide range of model parameters. We also demonstrate that the two-spin degeneracy of the Landau levels is lifted by the one-loop self-energy corrections. While the spin splitting of the damping rates is small, it may be important for some spin and chiral effects. We argue that the general method and the numerical results for the rates can have interesting applications in heavy-ion physics, astrophysics, and cosmology, where strongly magnetized QED or QCD plasmas are ubiquitous. Published by the American Physical Society 2024

Multivariate and Matrix-Variate Logistic Models in the Real and Complex Domains

Several extensions of the basic scalar variable logistic density to the multivariate and matrix-variate cases, in the real and complex domains, are given where the extended forms end up in extended zeta functions. Several cases of multivariate and matrix-variate Bayesian procedures, in the real and complex domains, are also given. It is pointed out that there are a range of applications of Gaussian and Wishart-based matrix-variate distributions in the complex domain in multi-look data from radar and sonar. It is hoped that the distributions derived in this paper will be highly useful in such applications in physics, engineering, statistics and communication problems, because, in the real scalar case, a logistic model is seen to be more appropriate compared to a Gaussian model in many industrial applications. Hence, logistic-based multivariate and matrix-variate distributions, especially in the complex domain, are expected to perform better where Gaussian and Wishart-based distributions are currently used.

On the Structure of SO(3): Trace and Canonical Decompositions

This paper is devoted to some selected topics of the theory of special orthogonal group SO(3). First, we discuss the trace of orthogonal matrices and its relation to the characteristic polynomial; on this basis, the partition of SO(3) formed by conjugation classes is described by trace mapping. Second, we show that every special orthogonal matrix can be expressed as the product of three elementary special orthogonal matrices. Explicit formulas for the decomposition as needed for applications in differential geometry and physics as symmetry transformations are given.

Near-Field Radiative Heat Transfer between Layered β-GeSe Slabs: First-Principles Approach.

The group-IV monochalcogenide monolayers, GeSe, are interesting and novel two-dimensional (2D) semiconductor materials due to their highly anisotropic physical properties. Monolayers of the different GeSe polymorphs have already had their physical properties and potential applications extensively investigated. However, few-layer homostructures, which can also be approximated as 2D systems in many cases, have not received the same attention. For this reason, in this work, we investigate the optical properties of a free-standing few-layer β-GeSe system and use this information to investigate their performance in the near-field radiative heat transfer (NFRHT). The required optical conductivity of the few-layer 2D material is calculated by using density functional theory (DFT), including spin-orbit coupling. The band structure is investigated for up to five layers, and the effective electron masses are calculated correspondingly. Using this information, both the intraband transitions due to the presence of free electrons introduced by doping and the interband transitions are considered. The contribution of the ionic vibrations is also included in calculating the optical properties because of its relevance to NFRHT through the resulting active optical phonons. With all these contributions included, more realistic predictions of the NFRHT between the layered 2D β-GeSe materials can be obtained. It is found that the heat transfer attainable with the layered system is similar to that of a single layer of β-GeSe we have obtained previously.

On the construction of various soliton solutions of two space-time fractional nonlinear models

Abstract In this article, we investigate a couple of nonlinear fractional models of eminent interests subsequently the conformable derivative sense is used to designate the fractional order derivatives. The given structures are transformed into nonlinear ordinary differential equations of integer order, and the extended simple equation technique is then employed to solve the resulting equations. Initially, the nonlinear space time fractional Klein–Gordon equation is considered emerging from quantum and classical relativistic mechanics, which have application in plasma physics, dispersive wave phenomena, quantum field theory, and optical fibres. Later, the (2 + 1)-dimensional time fractional Zoomeron equation is analysed which is convenient to explore the innovative phenomena related to boomerons and trappons. As a result, various new soliton solutions are successfully established. The reported results offer a key implementation for analysing the soliton solutions of nonlinear fractional models which are extremely encouraging arising in the recent era of science and engineering. The 3D simulations have been carried out to demonstrate dynamics of the various soliton solutions for a given set of parameters.

Combining Reinforcement Learning and Tensor Networks, with an Application to Dynamical Large Deviations.

We present a framework to integrate tensor network (TN) methods with reinforcement learning (RL) for solving dynamical optimization tasks. We consider the RL actor-critic method, a model-free approach for solving RL problems, and introduce TNs as the approximators for its policy and value functions. Our "actor-critic with tensor networks" (ACTeN) method is especially well suited to problems with large and factorizable state and action spaces. As an illustration of the applicability of ACTeN we solve the exponentially hard task of sampling rare trajectories in two paradigmatic stochastic models, the East model of glasses and the asymmetric simple exclusion process, the latter being particularly challenging to other methods due to the absence of detailed balance. With substantial potential for further integration with the vast array of existing RL methods, the approach introduced here is promising both for applications in physics and to multi-agent RL problems more generally.

Identification of Prospective Physics Teacher’s Problem Solving Skills: A Study on Simple Electrical Circuits Topic

Problem-solving is one of the essential and crucial skills for physical learning. This study describes the problem-solving skills of college students taking physics education programs and their incorrect answers on simple electrical circuits topic. We used four essay questions representing the series, parallel, combined series-parallel, and double-loop electrical circuits concepts to measure their problem-solving skills. Meanwhile, for describing their problem-solving skills, we used five categories of problem-solving processes, consisting of useful description (UD), physics approach (PA), specific application of physics (SAP), mathematical procedures (MP), and logical progression (LP). Further, to identify their mistakes, we examined the general mistakes in each problem-solving process. Our analysis results suggested that the college students taking the physics education program have relatively high problem-solving skills in the UD and PA categories. Still, they have low skills in SAP and MP categories, with poor ability in the LP category. Their mistakes were mostly observed in drawing complete electrical circuits and the circuits with missing and changing components (UD category), the correlation between each physic concept, law, and principle with the simple electrical circuits topic (PA category), confusion and inconsistency to correctly determine the + or – symbols for every source of voltage and difficulty in using the Kirchoff’s second law following the loop direction. Besides, the participants also frequently used incorrect V, and I value on the problem being reviewed (SAP category), did not re-evaluate the order and completeness of their answer, as well as the logical reasoning for the obtained V and I from their problem-solving process based on the scientific and physic concepts and characteristics (LP category).

Novel dynamical group-invariant solutions and conserved vectors of the Gilson–Pickering equation with applications in plasma physics

This work delves into the analysis of the Gilson–Pickering equation which governs the waves propagation in plasma physics by invoking Lie symmetry analysis. We commence by identifying the Lie point symmetries associated with the equation. These symmetries are then leveraged on to compute the commutator table and subsequently the adjoint representation, ultimately leading to the establishment of an optimal system of one-dimensional subalgebras. Each subalgebra within this system is subsequently utilized to perform symmetry reductions. Through these reductions, various forms of nonlinear ordinary differential equations are obtained, which are subsequently solved using the power series method and Kudryashov’s technique. The resulting solutions are given in terms of hyperbolic functions. To gain deeper insights into the behaviour of these solutions, three-dimensional and two-dimensional plots are presented. Furthermore, applying the Ibragimov’s theorem allows us to derive conserved vectors associated with the Gilson–Pickering equation.

A powerful tool for dealing with high-dimensional fractional-order systems with applications to fractional Emden–Fowler systems

In this study, an approximation solution for a high-dimensional system in terms of the Caputo fractional derivative operator is obtained using the improved modified fractional Euler method, or IMFEM for short. To accomplish this aim, a result that can transform such a system into a double-equation, one-dimensional fractional-order system, is provided theoretically. Some physical applications, including fractional-order systems of equations of Emden–Fowler type, are discussed, and their graphs are plotted using MATLAB to demonstrate the IMFEM schema’s validity.

Expanding chalcogen bonds in thiophenes to interactions with halogens.

Compounds containing the thiophene moiety find several applications in physics and chemistry, such as electrical conduction, which depends on specific conformations to properly exhibiting the desired properties. In turn, chalcogen bonding has found to modulate the conformation of some N-thiophen-2-ylfomamides. Since halogens participate in a kin interaction (halogen bonding) and are abundant in agrochemicals, pharmaceuticals, and materials, we have quantum-chemically explored the interaction between organic halogen and thiophene as a conformational modulator in some model compounds. Although such interaction indeed appears, as demonstrated by atoms in molecules and natural bond orbital analysis, it is inefficient to control the conformational equilibrium. An energy decomposition analysis scheme demonstrated that halomethane and thiophene tend to move away from one another due to a core component (Pauli repulsion and exchange), which is mainly due to a deformation term. Therefore, chalcogen bonds with halogens appear weaker than with other chalcogens.

Enhanced Design of a Tai Chi Teaching Assistance System Integrating DTW Algorithm and SVM

Physical education using technology has enabled traditional practices like Tai Chi, a martial art known for its multiple health benefits and meditative aspects, to set coordinated goals. This research presents an intelligent Tai Chi Teaching Assistance System supported by the integration of the Dynamic Time Warping algorithm and Support Vector Machine, in which can practitioners providing real-time feedback to improve Tai Chi learning and quality. In the system, the DTWA Dynamic Time Warping Algorithm was used to accurately compare a practitioner’s complex body movements with the Tai Chi standard movements dataset, taking into account execution speed deviations and others. Meanwhile, the SVM was employed to classify the movement as to quality and correctness, thereby being able to provide precise, individual feedback. This hybrid approach ensures a high-motion recognition accuracy rate while also adhering to nuanced Tai Chi requirements. The system was evaluated through detailed testing with various levels of Tai Chi experience. Evaluation showed that the students’ performance and understanding of most Taijiquan movements and related physical exercises improved significantly. It indicates the system has a practical application value for also beginners and intermediate and last expert, respectively. It also shows the effectiveness of combining DTW and SVM to support learners ‘body movement trajectory in a physical learning environment, opening them up to additional technology-assisted physical training applications. This provides implications for a more promising generation of future physical education involving the incorporation of complex AI technology.

Recent advances in employing the Laplace homotopy analysis method to nonlinear fractional models for evolution equations and heat-typed problems

This study stems from the growing need for effective mathematical tools to tackle nonlinear fractional evolution equations, which find wide applications in physics and engineering. Traditional methods often struggle to provide exact closed-form solutions for such equations, leading to the development of novel approaches. In this context, the combination of the homotopy analysis method with the Laplace transform presents a promising avenue for obtaining exact solutions to these complex equations. By exploring this hybrid approach, the study aims to address the existing challenges in solving nonlinear fractional models and provide a reliable method for obtaining closed-form solutions. In this way, this study focuses on rendering the coupling between the homotopy analysis approach and the Laplace transform in the search for exact closed-form solutions for the class of fractional evolution and heat-typed equations. In fact, this class of equations features some interesting nonlinear Caputo fractional partial differential equations that model dissimilar physical processes in physics and engineering. A generalized recurrent scheme is derived, which is applicable to all the members of the governing class, and further analyzed the application of this very scheme on some test fractional initial-value problems of much concern. Eventually, the method has been found to be advantageous, with various advantages, including convergence to available exact solutions. Thus, of the advantage of the current study is innovative combination of mathematical methods, the applicability of these methods to a special class of fractional equations, the development of a generalized scheme, the successful solution of prototype problems, and the practical advantages of the method.

Imaging Rovibrational Excitation of Scattered YO Molecules in Inelastic Collisions with Kr and Ne.

Energy transfer between atoms and molecules is fundamental to many physical and chemical processes, and understanding the mechanisms and outcomes of energy transfer is crucial for various applications in physics and chemistry. Here, the rovibrational excitation of YO(X 2Σ+) molecules with the collision of Kr and Ne has been studied in the laser-ablation crossed beam and time-sliced ion velocity map imaging setup in combination with the resonance enhanced multiphoton ionization scheme. Significant changes in the angular distribution for different rovibrational excitations of YO molecules are observed with the collision of Kr. The sharp forward distribution for low rovibrational excitation of YO(v' = 0, 1) molecules suggest that the weak attractive potential between Kr and YO is dominant at large impact parameters. Comparatively, the strong sideway distribution for highly rovibrationally excited YO(v' = 1, 2, 3, and 5) is due to rainbow scattering from the stronger attractive potential of Kr···YO at relatively small impact parameters. The more isotropic angular distribution in the highly rovibrationally excited YO(v' = 11) indicates the formation of a short-lived complex. A change in the angular distribution of scattered YO with different rovibrational excitations was also observed in the collisions of Ne. For YO as a heteronuclear diatomic molecule, collisions of the Y- and the O-end of YO with rare gases would affect the contribution of inelastic processes at different impact parameters.

Far UV coatings for liquid-Ar time projection chambers

Liquid Ar (LAr) and liquid Xe (LXe) time projection chambers (TPCs) are used for many applications in neutrino physics and direct dark matter searches. The performance of these detectors, particularly dual-phase ones, depends very strongly on the efficiency for detecting the far ultraviolet (FUV) scintillation light. Such detection is particularly challenging for LAr, in which the strongest scintillation feature is observed at a wavelength of 127 nm (175 nm for LXe). The current mainstream approach is covering the optical surfaces with a wavelength shifter, which absorbs de FUV light and emits at wavelengths that overlap with the optical band, where commercial devices have higher detection efficiency. This work presents coatings designed to enhance the optical properties of the detector materials and to be an alternative to the current technique. In particular, two possible coatings are proposed: narrowband and broadband FUV reflective coatings. The narrowband coatings are tuned at the FUV scintillation light. They provide a large reflectance at the design angle; additionally, these coatings are naturally transparent at longer wavelengths, which might be useful to selectively detect the wavelength of interest. Their performance is evaluated taking into account the refractive index of LAr and as a function of the angle of incidence. The same calculations are performed for an aluminium-based broadband mirror. Finally, the effect on reflectance of submerging both sorts of mirrors at liquid nitrogen temperature is presented.

Investigation into the physical characteristics of the compounds XBiSe2 (X = Li, Na or K).

As new materials, the ternary chalcogenides have recently brought scientists' attention. These materials are a novel class of semiconducting chemical compounds. They allow the increase of the photo-conversion efficiency, the performance, and the cheap energy cost. Such materials also provide a wide range of physical and chemical applications. The used investigation employs Density Functional Theory (DFT) implemented in the Wien2k package to systematically characterize the physical properties of ternary chalcogenide compounds XBiSe2 (X = Li, Na and K). Such method emphasizes their applicability to energy conversion technologies. Scrutinizing their electronic, optical, and thermoelectric properties elucidates the effect of alkali metal substitution on performance metrics. The results not only advance knowledge of these materials' physicochemical behaviors but also reveal their potential for tailored functionalization in next-generation energy and optoelectronic systems, marking a significant stride in material science and application-oriented research.

Exploring the impact of anode material on X-ray photon fluence and characteristics: A Monte Carlo simulation study

This study investigates the critical significance of anode material selection in defining the energy spectrum and properties of X-ray photons in medical physics applications. Using the GATE platform and Monte Carlo simulations, a direct relationship between anode material atomic number and photon fluence is demonstrated. As the atomic number increases from Z = 29 (Copper) to Z = 74 (Tungsten), photon fluence rises by 62 %, indicating a substantial impact on X-ray production. Furthermore, the X-ray spectrum is affected by this material-driven changes, revealing a noticeable shift towards higher energy values: the mean energy of the continuous spectrum rises from 46.97 keV for Copper to 49.0 keV for Tungsten. The thermal properties of the material affect the temperature increase at the focal point. Rhodium and Molybdenum have a higher temperature rise than Copper (Cu) and Tungsten (W), because Cu and W have a greater thermal diffusion compared to other materials. These findings underscore the significance of anode material choice in optimizing X-ray systems which may enhance diagnostic accuracy and efficiency in diverse applications.

Machine learning evaluation in the Global Event Processor FPGA for the ATLAS trigger upgrade

The Global Event Processor (GEP) FPGA is an area-constrained, performance-critical element of the Large Hadron Collider's (LHC) ATLAS experiment. It needs to very quickly determine which small fraction of detected events should be retained for further processing, and which other events will be discarded. This system involves a large number of individual processing tasks, brought together within the overall Algorithm Processing Platform (APP), to make filtering decisions at an overall latency of no more than 8ms. Currently, such filtering tasks are hand-coded implementations of standard deterministic signal processing tasks.In this paper we present methods to automatically create machine learning based algorithms for use within the APP framework, and demonstrate several successful such deployments. We leverage existing machine learning to FPGA flows such as hls4ml and fwX to significantly reduce the complexity of algorithm design. These have resulted in implementations of various machine learning algorithms with latencies of 1.2 μs and less than 5% resource utilization on an Xilinx XCVU9P FPGA. Finally, we implement these algorithms into the GEP system and present their actual performance.Our work shows the potential of using machine learning in the GEP for high-energy physics applications. This can significantly improve the performance of the trigger system and enable the ATLAS experiment to collect more data and make more discoveries. The architecture and approach presented in this paper can also be applied to other applications that require real-time processing of large volumes of data.