Physical Insight Research Articles

Native 2-D-FEM/Mode-Matching Formulation for Second-Order Symmetric Waveguide Devices

In this work we present, taking a holistic approach, the integration between the 2-D finite element method (FEM) and mode-matching (MM) for devices exhibiting second-order rotational symmetry and, optionally, first-order axial symmetry. Using a tailored approach, involving adequate inner products, the formulation allows for the rotationally symmetric modes with complex-valued eigenfunctions to keep their useful physical and mathematical properties. Mode class orthogonality and degeneracy discrimination are kept throughout the pipeline. The approach leads to a more efficient and accurate analysis of devices with these geometrical properties, significantly reducing the dimensionality of the problem, both in the inner-product calculation and in the generalized scattering matrix (GSM) derivation, while providing physical insight regarding modal discrimination.

Data Informed Model Test Design With Machine Learning – an Example in Nonlinear Wave Load on a Vertical Cylinder

Abstract Model testing is common in coastal and offshore engineering. The design of such model tests is important such that the maximal information of the underlying physics can be extrapolated with a limited amount of test cases. The optimal design of experiments also requires considering the previous similar experimental results and the typical sea-states of the ocean environments. In this study, we develop a model test design strategy based on Bayesian sampling for a classic problem in ocean engineering -- nonlinear wave loading on a vertical cylinder. The new experimental design strategy is achieved through a GP-based surrogate model, which considers the previous experimental data as the prior information. The metocean data are further incorporated into the experimental design through a modified acquisition function. We perform a new experiment, which is mainly designed by data-driven methods including several critical parameters such as the size of the cylinder and all the wave conditions. We examine the performance of such a method when compared to traditional experimental design based on manual decisions. This method is a step forward to a more systematic way of approaching test designs with marginally better performance in capturing the higher-order force coefficients. The current surrogate model also made several ‘interpretable’ decisions which can be explained with physical insights.

Effect of tissue viscoelasticity and adjacent phase-changed microbubbles on vaporization process and direct growth threshold of nanodroplet in an ultrasonic field

Understanding the behavior of nanodroplets converted into microbubbles with applied ultrasound is an important problem in tumor therapeutical and diagnostic applications. In this study, a comprehensive model is proposed to investigate the vaporization process and the direct growth threshold of the nanodroplet by following the vapor bubble growth, especially attention devoted to the effect of tissue viscoelasticity and adjacent phase-changed microbubbles (PCMBs). It is shown that the ultrasonic energy must be sufficiently strong to counterbalance the natural condensation of the vapor bubble and the tissue stiffness-inhibitory effect. The softer tissue with a lower shear modulus favors the vaporization process, and the nanodroplet has a lower direct growth threshold in the softer tissue. Moreover, the adjacent PCMBs show a suppression effect on the vaporization process due to the negative value of the secondary Bjerknes force, implying an attractive force, preventing the nanodroplet from escaping from the constraint of the adjacent PCMBs. However, according to the linear scattering theory, the attractive force signifies that the constraint is weak, causing the direct growth threshold to increase in the range of 0.09–0.24 MPa. The weak increase in threshold demonstrates that the direct growth threshold is relatively unaffected by the adjacent PCMBs. The prediction results of our model are in good agreement with the experiment results obtained by the echo enhancement method, in which the threshold is relatively independent of the intermediate concentration. The findings presented here provide physical insight that will be further helpful in understanding the complex behavior of the nanodroplet responses to ultrasound in practical medical applications.

Physical insight into thermal analysis of magnetohydrodynamic stagnation point flow of micropolar nanofluid across a flexible surface equipped with porous medium and Fourier and Fick's law

AbstractNanofluids represent a novel heat transfer liquid, making them an efficient medium for enhancing energy transmission. Nevertheless, significant knowledge gaps still exist concerning current strategies for improving heat transfer in nanofluids, underscoring the necessity for comprehensive research on these fluid systems. Therefore, this study considered theoretical analysis retrieves the influence of radiative two‐dimensional stagnation point flow of second‐grade micropolar fluid flow about an elongated channel surface implanted in porous media with magnetic effect, and modified heat and mass flux is under consideration. The major novel effect of the current study is to analyze the activation energy and thermal aspect of the system in the presence of nonlinear radiation effects that are considered in the revised mathematical framework by utilizing the boundary layer theory. The resulting set of coupled partial differential equations is further reduced and transformed into a dimensionless system of ordinary differential equations through appropriate scaling invariants. We initiate the RKF‐45 investigation scheme to numerically analyze the transformed dimensionless system, considering relevant parameters. The computational algorithm is implemented using MATLAB programming syntax. Plotted visuals are revealed for leading parameters against pertinent flow profiles graphically and with numerical data. Additionally, the convergence analysis of the numerical results for various flow profiles of the fluids were compared to establish the authenticity of the proposed flow problem. These research findings play a significant role in controlling heat transfer rates and fluid velocities in diverse manufacturing processes and industrial applications, ultimately aiding in achieving the desired product quality.

Analytic inclined flow rule for determining granular rheology under strong non-local effects

This study theoretically establishes a flow rule for a granular flow down a rough inclined plane, capable of determining granular rheology in the presence of strong non-local effects resulting from grain cooperativity. To describe the non-local rheology, a Landau–Ginzburg model is formulated in terms of the fluidisation parameter represented by the granular inertial number. The exact solutions of the inertial-number field are solved and provide physical insights into the evolution of the internal rheology and the flow arrest process controlled by the flow height. Through asymptotic analysis in the regime dominated by strong non-locality, the exact solutions are further reduced to yield an analytical flow rule for the mean flow velocity. A comparison between the prediction of the flow rule and experimental data from the literature for sand grains determines the underlying rheology law and the relevant rheological parameters. Thus, the proposed flow rule serves as an effective tool for inferring granular rheology from strongly non-local inclined flow data, surpassing the limitations of the classical flow rule deduced from the local rheology framework.

Implicit coupling methods for nonlinear interactions between a large‐deformable hyperelastic solid and a viscous acoustic fluid of infinite extent

AbstractThis paper addresses the challenges in studying the interaction between high‐intensity sound waves and large‐deformable hyperelastic solids, which are characterized by nonlinearities of the hyperelastic material, the finite‐amplitude acoustic wave, and the large‐deformable fluid–solid interface. An implicit coupling method is proposed for predicting nonlinear structural‐acoustic responses of the large‐deformable hyperelastic solid submerged in a compressible viscous fluid of infinite extent. An arbitrary Lagrangian–Eulerian (ALE) formulation based on an unsplit complex‐frequency‐shifted perfectly matched layer method is developed for long‐time simulation of the nonlinear acoustic wave propagation without exhibiting long‐time instabilities. The solid and acoustic fluid domains are discretized using the finite element method, and two different options of staggered implicit coupling procedures for nonlinear structural‐acoustic interactions are developed. Theoretical formulations for stability analysis of the implicit methods are provided. The accuracy, robustness, and convergence properties of the proposed methods are evaluated by a benchmark problem, that is, a hyperelastic rod interacting with finite‐amplitude acoustic waves. The numerical results substantiate that the present methods are able to provide long‐time steady‐state solutions for a nonlinear coupled hyperelastic solid and viscous acoustic fluid system without numerical constraints of small time step sizes and long‐time instabilities. The methods are applied to investigate nonlinear dynamic behaviors of coupled hyperelastic elliptical ring and acoustic fluid systems. Physical insights into 2:1 and 4:2:1 internal resonances of the hyperelastic elliptical ring and period‐doubling bifurcations of the structural and acoustic responses of the system are provided.

Process-structure multi-objective inverse optimisation for additive manufacturing of lattice structures using a physics-enhanced data-driven method

ABSTRACT Additive manufacturing (AM) has become a practical solution for fabricating lightweight and high-strength metallic lattice structures. The inverse optimisation of process-structure parameters to achieve high performance and minimised trial-and-error experiments has presented a persistent challenge. To address this problem, an inverse optimisation methodology has been proposed for coping with multiple conflicting performance objectives, consisting of mechanical properties and lightweight extent under AM-constraints. In the pursuit of greater accuracy, a physics-enhanced data-driven algorithm, i.e. encoding-stiffness-analysis multi-task Gaussian process regression, has been developed. This empowers us to precisely analyse how process-structure parameters impact the properties of AM-formed lattice structures. As an emerging machine learning method for AM, the physics-enhanced data-driven algorithm exhibits strong fitting capability and extrapolation performance, due to the interpretability provided by physical information. It has been applied as a surrogate model within the multi-objective genetic algorithm, facilitating the efficient design of parameters and the expansion of objective space. Notably, a deviation of less than 15% has been observed between predictive and experimental results, providing solid confirmation of our methodology's reliability. This confluence of physical insights and data-driven modelling holds substantial promise for accelerating the development of highly efficient designs.

Characteristic modal analysis in miniaturization and bandwidth‐enhancement of monopole antenna for WLAN/WiMAX/LTE applications

AbstractThis work presents a printed monopole antenna with a miniature size and enhanced wideband using characteristic mode analysis (CMA). The printed monopole antenna structure comprises one vertical and one horizontal monopole, and a ground plane with a vertical extension. By connecting the horizontal monopole to the ground plane extension through a via hole, a significant enhancement in bandwidth is achieved in both lower and high bands. A 105% increment in bandwidth is achieved with the via hole compared to without it. The influence of the via hole is analyzed using CMA to provide a physical insight into the design. The proposed antenna is compact with an electrical size of 0.01 λ2, leading to a miniaturized design.

Understanding structure-processing relationships in metal additive manufacturing via featurization of microstructural images

Understanding and predicting accurate property-structure-processing relationships for additively manufactured components is important for both forward and inverse design of robust, reliable parts and assemblies. While direct mapping of process parameters to properties is sometimes plausible, it is often rendered difficult due to poor microstructural control. Exploring the direct relationship between processing conditions and microstructural features can thus provide significant physical insights and aid the overall design process. Here, we develop an automated high-throughput framework to simulate an uncertainty-aware additive manufacturing (AM) process, characterize microstructural images, and extract meaningful features/descriptors. A kinetic Monte Carlo (KMC) based model of the AM process is used to simulate microstructural evolution for a diverse set of experimentally relevant processing conditions. We perform a parametric study to explore the relationship between microstructural features and processing conditions. Our results indicate that a many-to-one mapping can exist between processing conditions and typical descriptors; therefore, multiple descriptors are thus necessary to unambiguously represent microstructural images. Our work provides crucial quantitative and qualitative information that would aid in the selection of features for microstructural images. Featurized microstructures could then be utilized to build data-driven models for predictive control of microstructures and thereby properties of additively manufactured components.

Turbulence stabilization in tokamak plasmas with high population of fast ions

This letter provides a new physical insight into the fast ion effects on turbulence in plasmas having a high fast ion fraction and peaked fast ion density profile. We elucidate turbulence stabilization mechanisms by fast ions that result in internal transport barrier formation in the fast ion regulated enhancement mode plasma. Both linear and nonlinear gyrokinetic simulations show that the dominant turbulence suppression mechanisms are the dilution effects. In particular, we find that turbulence can be sufficiently suppressed solely by an inverted main ion density gradient due to fast ions, for the first time. New physical findings reported here improve our understanding of fast ion effects on turbulence, essential for fusion energy production where . Moreover, they will open up a new methodology to control plasma turbulence applicable to a wide range of plasma confinement regimes.

On the photoinduced degradation of tamsulosin: The simpler the better

In this work, we present a comprehensive investigation of the decomposition of tamsulosin. Experiments were performed in an aqueous solution through photolysis with a 254 nm light source. In general, tamsulosin degradation was observed (presenting a rate of 35.4%, in 180 min), demonstrating that direct photoinduced decomposition is efficient. The simpler uncatalyzed process worked while previous experiments using solid catalysts showed negligible degradation. The kinetic constant of the photolysis process (17.6 ×10−3 min−1) was determined, considering first order kinetics, by fitting plots of ln([Tam]t −[Tam]∞)/ln([Tam]0 −[Tam]∞) versus irradiation time, where [Tam]t is the concentration of tamsulosin at time t, and [Tam]∞ is the limiting concentration of the reacting system. In addition, physical insights regarding tamsulosin photoexcitation and photolysis were obtained through quantum chemistry computations. Density functional theory (DFT) was used to investigate the ground state while its time-dependent formalism (TD-DFT) was used for determining vertical excitation energies and generalized oscillator strengths (GOSs). All the computations were undertaken at the CAM-B3LYP/6-311++G(d,p) level of theory, in water. Regarding the excited states, all five lowest-lying states were determined to have non-zero GOSs, with S3 (found at 5.73 eV (∼216 nm) at the TD-DFT/CAM-B3LYP/6–311++G(d,p) level of theory) presenting the highest GOS (0.1157) among all. Hence, these accessible states may be assigned as those responsible for the photoinduced degradation observed experimentally. Moreover, the computational results suggest tamsulosin undergoes photodecomposition through excited state chemical reaction rather than via a direct photolysis path.

Numerical modeling of geometrically nonlinear responses of smart magneto-electro-elastic functionally graded double curved shallow shells based on improved FSDT

The use of functionally graded magneto-electro-elastic (FG-MEE) shells in smart composite structures like sensors, transducers, vibration monitoring, aeronautics, shape tracking, medical and sonar applications has a lot of promise. The accurate modeling approach for situations with several physics linked is quite difficult. In this paper, an improved first-order shear deformation hypothesis is used to inspect the magneto-electro-elastic coupling performance of thin-walled smart structures with laminate design presenting FGM composite as inactive material and using the magneto-piezoelectric patches as combined sensor and actuator features. The govern equations of motion, electric and magnetic distributions through the thickness direction of shell element are derived from the virtual of Hamilton principle. The simulations are carried out using a self-coded FE software. A numerical analysis of the nonlinear deflection of FG-MEE shells is performed under multiphysics loading. To ensure that the current MEE formulation is accurate, the findings were compared to existing solutions from the literature, and remarkable agreement was identified. A parametric analysis is conducted to demonstrate how the material composition affects the deflection throughout the thickness. Further, new numerical results, physical insights and finding have been provided in the present manuscript taking into account influences of several important parameters such as material graduation index and various boundary conditions. The novel MEE FE model suggested in this study is highly useful for the accurate development and design of FG-MEE structures.

Nonlinear aeroelastic fluid-structure-acoustic interaction analysis of a coupled composite panel with an acoustic cavity in supersonic flow

This paper is concerned with numerical modeling and nonlinear dynamics analyses of fluid-structure-acoustic interactions of an elastic composite laminated panel with a backed cavity in supersonic flow. To achieve strongly coupled solutions to the aeroelastic fluid-structure-acoustic interaction problem, an implicit partitioned arbitrary Lagrangian-Eulerian solution method is developed based on a finite volume model for the supersonic flow and a monolithic nonlinear finite element model for the composite panel-cavity system. A nonlinear higher-order shear deformation zig-zag theory is adopted to accommodate the large-deformable vibration of the composite panel, and the acoustic wave equation is employed for modeling the compressible fluid in the cavity. The method is verified by comparisons with benchmark results. Physical insights into the effects of the dynamic pressure, cavity depth, and the ply-angle of composite material on the aeroelastic behaviors of the panel-cavity system are provided. Some new phenomena are revealed. A special flutter behavior dominated by acoustic resonance is found for the composite panel with a backed cavity in supersonic flow, which is due to local mode coalescence as the mode frequencies of the acoustic cavity and the composite panel are close to each other. The transition between the aeroelastic flutters of the composite panel induced by the lower-order structural mode coalescence and the acoustic resonance of the cavity can be progressively adjusted by alerting the cavity depth and the ply-angle of the composite panel.

Adaptive Quantum State Tomography with Active Learning

Recently, tremendous progress has been made in the field of quantum science and technologies: different platforms for quantum simulation as well as quantum computing, ranging from superconducting qubits to neutral atoms, are starting to reach unprecedentedly large systems. In order to benchmark these systems and gain physical insights, the need for efficient tools to characterize quantum states arises. The exponential growth of the Hilbert space with system size renders a full reconstruction of the quantum state prohibitively demanding in terms of the number of necessary measurements. Here we propose and implement an efficient scheme for quantum state tomography using active learning. Based on a few initial measurements, the active learning protocol proposes the next measurement basis, designed to yield the maximum information gain. We apply the active learning quantum state tomography scheme to reconstruct different multi-qubit states with varying degree of entanglement as well as to ground states of the XXZ model in 1D and a kinetically constrained spin chain. In all cases, we obtain a significantly improved reconstruction as compared to a reconstruction based on the exact same number of measurements and measurement configurations, but with randomly chosen basis configurations. Our scheme is highly relevant to gain physical insights in quantum many-body systems as well as for benchmarking and characterizing quantum devices, e.g. for quantum simulation, and paves the way for scalable adaptive protocols to probe, prepare, and manipulate quantum systems.

Rotational Doppler Effect: A Review

AbstractGenerally, the linear motion between the source of a wave and an observer leads to a linear Doppler effect. It is associated with the linear momentum of the wave. For electromagnetic beams having a circular polarization or an azimuthal phase distribution, the rotation between the source and the observer results in a less well‐known rotational Doppler effect. It is associated with the angular momentum of the wave. This is particularly the case for vortex beams. Here, the various physical insights that are given to explain the origin of the rotational Doppler effect is reviewed. The focus is on different cases where such an effect gives information on the rotational nature of the probed systems, and also on cases where the rotational Doppler effect is useless. Still debated issues and possible applications are then presented.

Multimodal imaging-based material mass density estimation for proton therapy using supervised deep learning.

Mapping CT number to material property dominates the proton range uncertainty. This work aims to develop a physics-constrained deep learning-based multimodal imaging (PDMI) framework to integrate physics, deep learning, MRI, and advanced dual-energy CT (DECT) to derive accurate patient mass density maps. Seven tissue substitute MRI phantoms were used for validation including adipose, brain, muscle, liver, skin, spongiosa, 45% hydroxyapatite (HA) bone. MRI images were acquired using T1 weighted Dixon and T2 weighted short tau inversion recovery sequences. Training inputs are from MRI and twin-beam dual-energy images acquired at 120 kVp with gold/tin filters. The feasibility investigation included an empirical model and four residual networks (ResNet) derived from different training inputs and strategies by PDMI framework. PRN-MR-DE and RN-MR-DE denote ResNet (RN) trained with and without a physics constraint (P) using MRI (MR) and DECT (DE) images. PRN-DE stands for RN trained with a physics constraint using only DE images. A retrospective study using institutional patient data was also conducted to investigate the feasibility of the proposed framework. For the tissue surrogate study, PRN-MR-DE, PRN-DE, and RN-MR-DE result in mean mass density errors: -0.72%/2.62%/-3.58% for adipose; -0.03%/-0.61%/-0.18% for muscle; -0.58%/-1.36%/-4.86% for 45% HA bone. The retrospective patient study indicated that PRN-MR-DE predicted the densities of soft tissue and bone within expected intervals based on the literature survey, while PRN-DE generated large density deviations. The proposed PDMI framework can generate accurate mass density maps using MRI and DECT images. The supervised learning can further enhance model efficacy, making PRN-MR-DE outperform RN-MR-DE. The patient investigation also shows that the framework can potentially improve proton range uncertainty with accurate patient mass density maps. PDMI framework is proposed for the first time to inform deep learning models by physics insights and leverage the information from MRI to derive accurate mass density maps.

Extension of Judd-Ofelt theory: Application on Eu3+, Nd3+ and Er3+

We present a modified version of the Judd-Ofelt theory, which describes the intensities of f-f transitions for trivalent lanthanide ions (Ln3+) in solids. In our model, the properties of the dopant are calculated with well-established atomic-structure techniques, while the influence of the crystal-field potential is described as a perturbation, by three adjustable parameters. Compared to our previous work (Hovhannesyan et al., 2022 [28]), the spin-orbit interaction within the first excited configuration 4fw−15d is described in a perturbative way, whereas it is exactly taken into account in the ground configuration 4fw, using all the eigenvector components of the free-ion levels. Moreover, the wavelength-dependence of the refractive index of the host material is also accounted for. We test the validity of our model on three ions: Eu3+, Nd3+ and Er3+. The results of the extension are satisfactory, we are able to give a physical insight into all the transitions within the ground electronic configuration, and also to reproduce quantitatively experimental absorption oscillator strengths. We also performed calculations of standard JO parameters, and the results are in good agreement with the values reported in the literature. The code used to make the calculations is available on GitLab.

Paving the road towards ITER relevant long deuterium pulses at ELISE by investigating improved operational scenarios

Hydrogen plasma pulses of 1000 s with an extracted current density of over 90 % of the ITER target value (while also fulfilling the requirements for the electron-ion ratio and the global top/bottom beam homogeneity) were demonstrated at the ELISE test facility with its half ITER-size negative hydrogen ion source in pulsed extraction mode, i.e. short beam extraction phases of ≈10 s each ≈150 s, and using the so-called potential rods. In deuterium roughly 67 % of the ITER target for the extracted current density has been achieved for pulses longer than 45 minutes, limited by a pronounced vertical asymmetry and increase with timeinthe co-extracted electron current.In order to stabilize the co-extracted electron current during long pulses, particularly in deuterium, improved operational scenarios have to be developed. At ELISE, it was investigated the possibility to remove the potential rods and instead to set the bias plate to a positive voltage with respect to the source vessel. In this operational scenario short pulses in deuterium can be achieved with an extracted negative ion current density of up to 90 % of the ITER target value.In order to improve the physics insight, a 2D fluid code is applied. The first general result is that the modified potentials of surfaces close to the extraction system strongly affect the fluxes of charged particles towards different surfaces and consequently can result in a reduction of the co-extracted electron current.The temporal behaviour during long pulses with pulsed extraction, in particular of the co-extracted electrons, demonstrates a significant impact of the beam extraction on the caesium dynamics. Thus, CW extraction is mandatory for developing fully ITER relevant operational scenarios. After an extensive upgrade, ELISE is now capable of performing CW pulses, i.e. long-pulse beam extraction.

Parameter reduction for environmental inversions in ocean acoustics

Environmental inversions in ocean acoustics require the simultaneous estimation of many unknown parameters. Unfortunately, many environmental parameters are unidentifiable from available measurements. Recent work in statistical physics has developed tools for analyzing such models using information geometry. The Manifold Boundary Approximation (MBAM) systematically prunes unidentifiable parameters to produce an identifiable model that can be analyzed with traditional statistical methods. In this talk, we report on progress applying MBAM to underwater acoustics models forenvironmental inversion, focusing on transmission loss (TL) in range-independentnormal mode models. The key technical advance is the calculation of geodesics (a generalization of straight lines to curved surfaces) on a geometric representation of the model known as the model manifold. Geodesics inform which environmental parameters are constrained by ocean sounds and guide the removal of unidentifiable parameters. The result is a simplified, identifiable model of comparable accuracy. We summarize physical insights revealed by MBAM applied to ocean inversion. [Work supported by Office of Naval Research.]

Mechanism of slow-wave generation and tuning in a perforation-modulated sonic black hole structure

A sonic black hole (SBH) in a retarding duct can be used to manipulate sound waves and achieve sound absorption. The SBH incorporates two indispensable physical processes: wave speed reduction and energy dissipation. In this study, a perforation-modulated SBH (PMSBH) retarding structure is proposed, in which the two physical phenomena can be balanced to enhance the black hole effects by modulating the perforation parameters. To fully investigate the mechanism of slow-wave generation and the effect of perforation parameters, an analytic model is established based on the Wentzel–Kramers–Brillouin (WKB) solutions to the linear acoustic wave equation. By examining the analytical solutions and comparing them with the numerical results obtained from transient finite element simulations, the effect of several important parameters on reducing the sound speed and enhancing sound absorption performance is revealed, offering a set of criteria and physical insights for modulating the parameters to design an optimal slow-sound absorber. Finally, an experiment is conducted to confirm the theoretical studies and demonstrate the performance of PMSBH. This study brings forward the concept of tunable design to improve the performance of SBH structures, which can benefit the design of sound wave manipulation and noise control devices.