Physical Insight Research Articles

Numerical investigation of departure from nucleate boiling for subcooled flow boiling in a rolling tube

A crucial issue in floating nuclear plants is the critical heat flux (CHF) under oscillatory conditions by ocean waves. CHF must be predicted considering the dynamic motion of the floating platform. We present numerical simulations for predicting the DNB (departure-from-nucleate-boiling)-type CHF of subcooled R134a flow in a rolling tube and provide physical insight into the dynamic flow behavior regarding the DNB, considering rolling amplitudes of 15°, 30°, and 40° and a rolling period of 6 s. The local wall temperature and void fraction depended mainly on the dynamic changes in the gravitational direction. Gravity had the effect of lowering DNB. The Euler force created a secondary liquid flow that somewhat reduced the liquid temperature at the heating wall during the rolling period, thereby enhancing the convective and quenching heat fluxes. Furthermore, the secondary flow rapidly increased the liquid area fraction at the overheated wall and decreased the temporarily increased wall temperature before DNB occurred. The secondary flow had the effect of increasing DNB. In this study, the effect of increasing the DNB value is greater than the effect of reducing the DNB value. The occurrence of DNB depended on sufficient cooling of the downward-facing wall during the rolling period.

Two-dimensional analytical model for fully depleted SOI MOSFETs with vertical trapezoid doping including effects of the interface trapped charges

The two-dimensional (2D) potential distribution for vertical trapezoidal doping thin-body fully depleted (FD) silicon-on-insulator (SOI) devices is derived by adopting the evanescent mode analysis method, in which the 2D effects in gate oxide region, channel region and buried oxide region are taken into account. Moreover, the effects of interface trapped charge are considered. Using this potential model, the subthreshold performance of the device including subthreshold current, and subthreshold swing under various conditions have been studied. The result shows that the analytical model is good agreement with the simulated results. Therefore, it provides a feasible way of developing new 2D models for vertical trapezoidal doping thin-body FD SOI devices. Besides offering the physical insight into device physics, the analytical model provides the basic designing guidance for vertical trapezoidal doping thin-body FD SOI devices.

MCNOX: A code for computing and interpreting ultrafast nonlinear X-ray spectra of molecules at the multiconfigurational level

This work describes a program for computing and analyzing the ultrafast (attosecond and femtosecond) nonlinear X-ray spectra of molecules at the multiconfigurational quantum chemistry level, called MCNOX. It is aimed at cutting-edge current and future photochemistry/photophysics applications enabled by X-ray free-electron lasers and high harmonic generation light sources. It can compute steady-state X-ray absorption spectroscopy (XAS) and three types of ultrafast nonlinear X-ray spectra: transient XAS, all-X-ray four-wave mixing, and stimulated Raman spectra. It is especially capable of picking out major electronic transitions, and further computing the natural transition orbitals for these transitions, which help finally yield the physical and chemical insights from complex signals. Following a research paradigm of “electronic structure→molecular dynamics→signal”, in this paper, methods for the former two steps are reviewed, and then the theory, implementations, and technical details for signal simulations are presented along illustrative examples on uracil.

Diffuser optimization for enhancing light extraction from light-emitting electrochemical cells

Light-emitting electrochemical cells (LECs) have enabled many novel solution-processable, low-cost, and flexible emission applications. Nevertheless, relatively inferior device efficiencies render LECs less competitive with other emission technologies. Light extraction would be a promising approach to enhance the device efficiencies of LECs. The diffuser film composed of a transparent photoresist layer doped with large and small TiO2 nanoparticles (NPs) embedded between the indium-tin-oxide layer and the glass substrate has shown superior light extraction performance (>2.5X). However, the physical insight of optimization, e.g., adjusting the concentration of small TiO2 NPs in diffuser film, has not been explored yet. In this work, increasing the concentration of small TiO2 NPs is found to increase the thickness, roughness, and refractive index of the diffuser film. The optical simulation indicates that the thickness of the diffuser film has little effect on the light extraction. However, a higher roughness but a lower refractive index is preferred for the diffuser film to achieve better light extraction. As such, it is rational to expect that a proper concentration of small TiO2 NPs to reach a high enough roughness but still keep a relatively low refractive index is required for diffuser optimization. This study clarifies the physical mechanism responsible for diffuser optimization and thus shows a high potential for lighting applications of LECs.

Flavor-dependent U(1) extension inspired by lepton, baryon and color numbers

There is no reason why the gauge symmetry extension is family universal as in the standard model and the most well-motivated models, e.g. left-right symmetry and grand unification. Hence, we propose a simplest extension of the standard model – a flavor-dependent U(1) gauge symmetry – and find the new physics insight. For this aim, the U(1) charge, called X, is expressed as X=xB+yL\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$X=x B+y L$$\\end{document} in which x and y are free parameters as functions of flavor index, e.g. for a flavor i they take xi\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$x_i$$\\end{document} and yi\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$y_i$$\\end{document} respectively, where B and L denote normal baryon and lepton numbers. Imposing a relation involved by the color number 3, i.e. -x1,2,…,n=xn+1,n+2,…,n+m=3y1,2,…,n+m≡3z\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-x_{1,2,\\ldots ,n}=x_{n+1,n+2,\\ldots ,n+m}=3y_{1,2,\\ldots ,n+m}\\equiv 3z$$\\end{document}, for arbitrarily nonzero z, we achieve a novel U(1) theory with implied X-charge. This theory not only explains the origin of the number of observed fermion families but also offers a possible solution for both neutrino mass and dark matter, which differs from B-L\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$B-L$$\\end{document} extension. Two typical models based on this idea are examined, yielding interesting results for flavor-changing neutral currents and particle colliders, besides those of neutrino mass and dark matter.

Analytical Approach to Two‐Dimensional Full Reflections of Fast Magnetosonic Waves Due To Mesoscale Density Boundaries

AbstractPropagation of fast magnetosonic (MS) waves in the equatorial plane has attracted a growing attention in recent studies. Although computer simulations including ray tracing, PIC, and full wave simulations have been utilized to study the propagation of MS waves, analytical approach is needed to investigate the physics insight. In this letter, we first derive the wave equations describing the two‐dimensional propagation of MS waves under the assumption that the mesoscale density inhomogeneity is only along one dimensional. After investigating the essential role of the potential function in the wave equation for the full reflection of MS waves, we find that full reflection can occur when the minimum potential is significantly negative. Finally, we have discussed the effects of incidence angles and wave frequencies on the full reflection. Our results show that MS waves with an incidence angle or wave frequency greater than the critical value would be fully reflected.

Physical insights in thermo-elastic damping and critical magnetic field of an embedded micro/nanobeam resonator

This article investigates the behavior of thermoelastic damping in a micro/nano beam subjected to an external magnetic field and an elastic matrix via small-scale theory. The material properties of the beam, such as Young’s modulus, thermal conductivity, specific heat capacity, and coefficient of thermal expansion, are considered to analyze the physical aspects of thermoelastic damping. Further, the critical magnetic field strength of the nanobeam, which affects magnetic properties, is studied in relation to temperature, magnetic material properties, modenumber, and boundary conditions, leading to important findings for magnetomechanical systems and materials design.

A new mechanism for internal kink stabilization by plasma flow and anisotropic thermal transport

Stability of the internal kink (IK) mode is numerically investigated with inclusion of the anisotropic thermal transport effect and the toroidal plasma flow. It is found that anisotropic thermal transport, in combination with plasma flow, stabilizes the IK in two ways. One is direct stabilization of the mode synergistically with plasma flow. The other, indirect stabilization involves generation of a finite mode frequency in static plasmas by thermal transport, which in turn invokes wave-wave resonance damping of the mode via interaction with stable shear Alfvén waves. This second IK stabilization mechanism is further corroborated by examining the eigenmode structure, which peaks at the radial locations where the mode frequency matches that of the shear Alfvén wave. Finally, two branches of unstable IK are identified, with mode conversion occurring at certain plasma flow speed and thermal transport level. These findings provide new physics insights in the IK stability in tokamak fusion plasmas.

Insights into the Mechanism of Tryptophan Fluorescence Quenching due to Synthetic Crowding Agents: A Combined Experimental and Computational Study.

Intrinsic tryptophan fluorescence spectroscopy is an important tool for examining the effects of molecular crowding and confinement on the structure, dynamics, and function of proteins. Synthetic crowders such as dextran, ficoll, polyethylene glycols, polyvinylpyrrolidone, and their respective monomers are used to mimic crowded intracellular environments. Interactions of these synthetic crowders with tryptophan and the subsequent impact on its fluorescence properties are therefore critically important for understanding the possible interference created by these crowders. In the present study, the effects of polymer and monomer crowders on tryptophan fluorescence were assessed by using experimental and computational approaches. The results of this study demonstrated that both polymer and monomer crowders have an impact on the tryptophan fluorescence intensity; however, the molecular mechanisms of quenching were different. Using Stern-Volmer plots and a temperature variation study, a physical basis for the quenching mechanism of commonly used synthetic crowders was established. The quenching of free tryptophan was found to involve static, dynamic, and sphere-of-action mechanisms. In parallel, computational studies employing Kohn-Sham density functional theory provided a deeper insight into the effects of intermolecular interactions and solvation, resulting in differing quenching modes for these crowders. Taken together, the study offers new physical insights into the quenching mechanisms of some commonly used monomer and polymer synthetic crowders.

Universal adaptive optics for microscopy through embedded neural network control

The resolution and contrast of microscope imaging is often affected by aberrations introduced by imperfect optical systems and inhomogeneous refractive structures in specimens. Adaptive optics (AO) compensates these aberrations and restores diffraction limited performance. A wide range of AO solutions have been introduced, often tailored to a specific microscope type or application. Until now, a universal AO solution – one that can be readily transferred between microscope modalities – has not been deployed. We propose versatile and fast aberration correction using a physics-based machine learning assisted wavefront-sensorless AO control (MLAO) method. Unlike previous ML methods, we used a specially constructed neural network (NN) architecture, designed using physical understanding of the general microscope image formation, that was embedded in the control loop of different microscope systems. The approach means that not only is the resulting NN orders of magnitude simpler than previous NN methods, but the concept is translatable across microscope modalities. We demonstrated the method on a two-photon, a three-photon and a widefield three-dimensional (3D) structured illumination microscope. Results showed that the method outperformed commonly-used modal-based sensorless AO methods. We also showed that our ML-based method was robust in a range of challenging imaging conditions, such as 3D sample structures, specimen motion, low signal to noise ratio and activity-induced fluorescence fluctuations. Moreover, as the bespoke architecture encapsulated physical understanding of the imaging process, the internal NN configuration was no-longer a “black box”, but provided physical insights on internal workings, which could influence future designs.

Physically constrained learning of MOS capacitor electrostatics

In recent years, neural networks have achieved phenomenal success across a wide range of applications. They have also proven useful for solving differential equations. The focus of this work is on the Poisson–Boltzmann equation (PBE) that governs the electrostatics of a metal–oxide–semiconductor capacitor. We were motivated by the question of whether a neural network can effectively learn the solution of PBE using the methodology pioneered by Lagaris et al. [IEEE Trans. Neural Netw. 9 (1998)]. In this method, a neural network is used to generate a set of trial solutions that adhere to the boundary conditions, which are then optimized using the governing equation. However, the challenge with this method is the lack of a generic procedure for creating trial solutions for intricate boundary conditions. We introduce a novel method for generating trial solutions that adhere to the Robin and Dirichlet boundary conditions associated with the PBE. Remarkably, by optimizing the network parameters, we can learn an optimal trial solution that accurately captures essential physical insights, such as the depletion width, the threshold voltage, and the inversion charge. Furthermore, we show that our functional solution can extend beyond the sampling domain.

Ultrasonic vibration-assisted enhanced antibacterial activity of ZnO/Chitosan bio-nanocomposite

This work delves into biopolymeric membrane-based piezo-catalyst and its effectiveness towards harmful gram positive (E. faecalis) and gram-negative (E. coli) pathogenic bacteria. Conventionally, piezo electric polymers and their modified nanocomposites have been used to eradicate pathogens by their functionality. Maybe first-time bio-compatible polymer (Chitosan) based nanocomposite (ZnO/Chitosan) has been used in this work. ZnO nanoparticle has been synthesized by hydrothermal technique and prepared hydrogel with Chitosan biopolymer. The nanoparticles and its modified counterparts have been characterized by conventional characterization techniques such as XRD, FTIR, FESEM, EDAX, TGA, UV–Vis, PL, Zeta potential etc. The structural assessment via X-Ray crystallography and FTIR validate the successful preparation of the nanocomposite. Electron microscopy, optical and thermal characterizations are used to probe the underlying physical insights of the specimen under study. A detailed examination also revealed that the catalytic process is accelerated by the production of reactive oxygen species particularly OH* during ultrasonic vibration, validated through Photoluminescence spectroscopy. This intriguing outcome led us to apply the piezo catalytic process-which has a drastic bactericidal impact (almost 96 % for E. faecalis and 98 % for E. coli killing after 20 min) and is developing as a substitute antibacterial method to eliminate pathogenic bacteria.

A numerical investigation of an indoor dry storage facility on heat transfer

This study conducted a numerical simulation of the dry storage system and indoor facility heat transfer problem. Four standard DSS models have been constructed to investigate the thermal performance. The results show a high-temperature difference found on the surfaces between the sealing canister and the cask, indicating a high thermal resistance in the radial direction. It is also found that the boundary conditions of DSS only affect the indoor facility ambient parameters instead of the inside components. The adjacent pitch distance between DSSs does not significantly change the indoor temperature but affects the view factor. However, convection accounts for 90% of the heat transfer, while radiation only contributes less than 4%. Thus, it is not essential to emphasize the indoor DSS arrangement optimization. This is the first time indoor facility DSS simulation offers suggestions and physical insights into DSS heat transfer, which is beneficial to nuclear energy science and technology.

Hybrid Approach Using Physical Insights and Data Science for Stuck-Pipe Prediction

Summary Early detection of stuck-pipe incidents is crucial because of the enormous costs of recovering from such incidents. Previous studies have leaned significantly toward a physics-based or data science approach. However, both approaches have challenges, such as the uncertainty of the physics-based model and the lack of data in the data science approach. In this study, we propose a hybrid approach using physical insights and data science and discuss the possibility of stuck-pipe prediction. The proposed method comprises two steps. In the first step, a data-driven model with physical insights is trained using the historical data of the in-situ well to estimate some of the drilling variables. In the second step, the risk of stuck-pipe occurrence (hereafter referred to as sticking risk) is calculated based on the historical and current measured data and the estimation of the trained model. This approach is expected to overcome the limitations of the previous methods as it allows the construction of a detection model tuned to the in-situ well. In the case studies, models for estimating the topdrive torque and standpipe pressure were constructed. The performance of the models is discussed using actual drilling data from drilling fields, including 21 stuck-pipe incidents during drilling operations. The proposed method was first examined using short-term output. The output confirmed that the sticking risk increased shortly (up to 20 hours) before the stuck-pipe incident occurred in 15 cases. This increase in sticking risk was consistent with physical considerations. Subsequently, this study examined the long-term output over several months; this was rarely done in previous studies. Even within this long-term output, some cases had good performance with only a few false alarms, while others had problems with many false alarms. For cases of low performance, several model improvements, such as adding mud information or making the learning and threshold-setting methods more robust to outliers, were found to have the potential to improve performance. The novelty of our research lies in creating a broad framework for the stuck-pipe prediction by using both physical insights and data science methods. The proposed hybrid approach demonstrated the potential to reduce false alarms and improve interpretability compared with previous methods. The framework is highly extensible, and further performance improvements can be expected in the future.

Deep trap assisting elastico-mechanoluminescence in diphase non-piezoelectrics induced by tunneling effect

The sustainable conversion of mechanical energy into light (elastico-mechanoluminescence, EML) opens up possibilities for energy-saving, which is of pivotal significance in addressing the energy crisis. The concepts of piezophotonics and the piezoelectric field's dependence on the probability of charge carriers detrapping have been thoroughly developed in explaining EML. Nevertheless, in contrast to the EML triggered by the piezoelectricity model, strong elastico-mechanoluminescence phenomena have also been frequently discovered in non-piezoelectric materials. Is the working principle different? This paper provides physical insight into the reconfigurable EML phenomena of intrinsic non-piezoelectric systems. It emphasizes the exploration of the mechanism through comprehensive analysis of trap information, de-trapping processes, and the lifetime of charge carriers in traps. We demonstrate the assistance of deep trap to enhance the red EML mode in a diphase centrosymmetric luminescent host through the electron tunneling effect. This advancement supports the progress of non-piezoelectric EML dielectrics and offers an appealing alternative approach in this field.

Anisotropic honeycomb stack metamaterials of graphene for ultrawideband terahertz absorption

Abstract Graphene aerogels have implied great potential for electromagnetic wave absorption. However, the investigation of their design for broadband absorption in the terahertz (THz) range remains insufficient. Here, we propose an anisotropic honeycomb stack metamaterial (AHSM) based on graphene to achieve ultrawideband THz absorption. The absorption mechanism is elucidated using the effective medium method, offering deeper physics insights. At low THz frequencies, the impedance matching from the air to the AHSM can be improved by reducing the chemical potential of graphene for high absorption. There is a suppression of absorption at the intermediate frequencies due to constructive interference, which can be avoided by shortening the sizes of honeycomb edges. With the aim to elevate absorption at high frequencies, one can increase the stack layer number to enhance multiple reflections and destructive interference within the metastructure. Based on the above principles, we design an AHSM that achieves a broadband absorbance of over 90 % from 1 THz to 10 THz. This absorption can tolerate a wide range of incident angles for both TE and TM wave excitations. Our research will provide a theoretical guide to future experimental exploration of graphene aerogels for THz metamaterial absorber applications.

Solution of FPK equation for stochastic dynamics subjected to additive Gaussian noise via deep learning approach

The Fokker-Plank-Kolmogorov (FPK) equation is an idealized model representing many stochastic systems commonly encountered in the analysis of stochastic structures as well as many other applications. Its solution thus provides an invaluable insight into the performance of many engineering systems. Despite its great importance, the solution of the FPK equation is still extremely challenging. For systems of practical significance, the FPK equation is usually high dimensional, rendering most of the numerical methods ineffective. In this respect, the present work introduces the FPK-DP Net as a physics-informed network that encodes the physical insights, i.e. the governing constrained differential equations emanated out of physical laws, into a deep neural network. FPK-DP Net is a mesh-free learning method that can solve the density evolution of stochastic dynamics subjected to additive white Gaussian noise without any prior simulation data and can be used as an efficient surrogate model afterward. FPK-DP Net uses the dimension-reduced FPK equation. Therefore, it can be used to address high-dimensional practical problems as well. To demonstrate the potential applicability of the proposed framework, and to study its accuracy and efficacy, numerical implementations on five different benchmark problems are investigated.

Molecular dynamics calculations of the enthalpy of vaporization for different water models

Phase-change heat transfer processes involving water play a key role in numerous societal applications, such as electrical power generation, microelectronics cooling, or water desalination. Advanced computational tools, such as molecular dynamics (MD), are increasingly being used to simulate these processes, especially on nanostructured surfaces and in nanoparticle suspensions, to gain insight into the unique nanoscale physics at play. In these studies, it is critical to use models of the water molecule that yield accurate predictions of water’s thermophysical properties, particularly its enthalpy of vaporization. Here, we use MD to determine the enthalpy of vaporization (hfg) for six common water models and four different values of the cutoff radius (i.e., the intermolecular distance beyond which interactions are simplified or ignored). The most accurate prediction of hfg (within 2 % of the value tabulated by ASHRAE) comes from the TIP3P water model with a cutoff radius of 14 Å. We also conducted a preliminary study of the variation of hfg with pressure and found reasonable agreement between simulations and tabulated ASHRAE data (<7% error). This work provides physical insights into the sensitivity of the predicted macroscopic thermophysical properties of water to minute changes in the atomic-level properties and is expected to inform future MD-based studies of phase change processes of water, especially as they occur at elevated pressures and on nanostructured surfaces and in nanoconfined geometries.

Damage assessment of laminated composites using unsupervised autonomous features

This article proposes a framework for the damage assessment of and effect of temperature variations in laminated composites using Lamb waves and unsupervised autonomous features. A network of piezoelectric transducers is employed to generate data for 18 health states of a laminated composite plate. The data is processed with sparse autoencoder (SAE) for unsupervised autonomous features. The discriminative capabilities of the extracted features are confirmed by processing the feature space in the supervised and unsupervised frameworks of machine learning. The confusion matrices of supervised learning provided physical insights into the problem. The feature space was also visualized in two dimensions in an unsupervised manner through principal component analysis (PCA), which revealed physically consistent results for the effect of temperature variations, damage of different severity levels, and the undamaged paths between the actuator and sensors. The healthy state data and information on the paths between the actuator and sensors was processed via SAE for damage localization. The proposed approach can be employed for the autonomous assessment of composite structures for the presence of damage and variations of operating temperatures while using both supervised and unsupervised machine learning algorithms.

Principles of progressive slow-sound and critical coupling condition in broadband sonic black hole absorber

Recent advances in sonic black hole (SBH) provide new opportunities for controlling sound waves and designing wave manipulation devices. SBH is a device that consists of partitions with gradually decreasing inner radii inserted into an acoustic duct. Several studies have reported that SBH can achieve a broadband sound absorption coefficient close to 1, avoiding the issue of alternating high and low absorption coefficients observed in traditional sound absorbers. However, the fundamental mechanisms and principles behind this behavior are not yet fully understood. This study aims to investigate the detailed sound absorption mechanisms of SBH, including the progressive slow-sound effect and the critical coupling condition that leads to broadband sound absorption. To achieve this goal, an analytical model based on the effective medium approach is developed to investigate the layer-by-layer retardation in sound propagation. The sound absorption coefficient is then determined based on the surface impedance calculation. The effective medium analysis reveals that SBH enables a unique condition to progressively decelerate wave propagation across its layers. As a result, the critical coupling condition becomes more easily established with smoothly increasing SBH partitions and more discretised layers, as elucidated by the complex frequency analysis results. The physical insights gained from this study reveal the distinctive features of SBH compared to classical sound absorbers, paving the way for its engineering applications.