Mode Dynamics Research Articles

Correlated measurement of neutron multiplicity, fission fragment mass, and total kinetic energy distributions: Reaction dynamics of different fission modes

Correlated measurement of neutron multiplicity, fission fragment mass, and total kinetic energy distributions: Reaction dynamics of different fission modes

Singular-perturbation-based estimation of nominal input gain in lightly damped systems with multiple modes

Active disturbance rejection controllers have been widely employed for governing a class of high-dimensional lightly damped systems with multiple eigenmodes. Nevertheless, previous studies rarely focused on the impact of neglected fast resonant mode dynamics on the nominal input gain, especially if high-frequency unmodeled resonant dynamics gradually approach the target control bandwidth. Here, we have quantitatively analyzed the effect of fast resonant mode dynamics on the nominal input gain using the singular perturbation method. A practical method for estimating the nominal input gain is provided for various control bandwidths. The stability bound associated with the fast resonant mode and coupling dynamics between the slow and fast resonant modes is also investigated. Finally, the validity and superiority of the proposed method are demonstrated through simulations.

Bosonic Pauli+: Efficient Simulation of Concatenated Gottesman-Kitaev-Preskill Codes

A promising route towards fault-tolerant quantum error correction is the concatenation of a Gottesman-Kitaev-Preskill (GKP) code with a qubit code. Development of such concatenated codes requires simulation tools which realistically model noise, while being able to simulate the dynamics of many modes. However, so far, large-scale simulation tools for concatenated GKP codes have been limited to idealized noise models and GKP code implementations. Here, we introduce the Bosonic Pauli+ model (BP+), which can be simulated efficiently for a large number of modes, while capturing the rich dynamics in the bosonic multi-mode Hilbert space. We demonstrate the method by simulating a hybrid surface code, where the data qubits are finite-energy GKP qubits stabilized using the small-Big-small (sBs) protocol, and the syndrome qubits are standard two-level systems. Using BP+, we present logical error rates of such an implementation. Confidence in the accuracy of the method is gained by comparing its predictions with full time evolution simulations for several relevant quantum circuits. While developed specifically for GKP qubits stabilized using the sBs protocol, the mathematical structure of BP+ is generic and may be applicable also to the simulation of concatenations using other bosonic codes.

Characterization of the I-phase regime at TCV

Abstract The I-phase is an H-mode confinement regime of tokamaks characterized by limit cycle oscillations, the so-called LCOs or bursts. These bursts are the manifestation of a periodic flattening of the plasma edge pressure profile. The profile flattening is caused by increased radial transport, driven by a high-frequency plasma edge mode that periodically appears. This short-living mode is intrinsically connected to each burst. It vanishes once the profiles are fully flattened, and it reestablishes during profile recovery once critical gradients are reached and a new cycle begins.
In this paper, we describe for the first time the unambiguous presence of the I-phase at the tokamak à configuration variable (TCV). As the I-phase confinement regime is found in the parameter regime between the L-mode and the fully developed H-mode, it is often confused with dithers between H-mode and L-mode. Therefore, we are highlighting the differences between these two phenomena. Furthermore, we show the two-dimensional dynamics of the I-phase mode and bursts and the associated filamentary transport, enabled by the outstanding capabilities of the 2D TCV Gas Puff Imaging (GPI) diagnostics.

Electrophysiological dynamics of salience, default mode, and frontoparietal networks during episodic memory formation and recall revealed through multi-experiment iEEG replication.

Dynamic interactions between large-scale brain networks underpin human cognitive processes, but their electrophysiological mechanisms remain elusive. The triple network model, encompassing the salience network (SN), default mode network (DMN), and frontoparietal network (FPN), provides a framework for understanding these interactions. We analyzed intracranial electroencephalography (EEG) recordings from 177 participants across four diverse episodic memory experiments, each involving encoding as well as recall phases. Phase transfer entropy analysis revealed consistently higher directed information flow from the anterior insula (AI), a key SN node, to both DMN and FPN nodes. This directed influence was significantly stronger during memory tasks compared to resting state, highlighting the AI's task-specific role in coordinating large-scale network interactions. This pattern persisted across externally driven memory encoding and internally governed free recall. Control analyses using the inferior frontal gyrus (IFG) showed an inverse pattern, with DMN and FPN exerting higher influence on IFG, underscoring the AI's unique role. We observed task-specific suppression of high-gamma power in the posterior cingulate cortex/precuneus node of the DMN during memory encoding, but not recall. Crucially, these results were replicated across all four experiments spanning verbal and spatial memory domains with high Bayes replication factors. Our findings advance understanding of how coordinated neural network interactions support memory processes, highlighting the AI's critical role in orchestrating large-scale brain network dynamics during both memory encoding and retrieval. By elucidating the electrophysiological basis of triple network interactions in episodic memory, our study provides insights into neural circuit dynamics underlying memory function and offer a framework for investigating network disruptions in memory-related disorders.

Electrophysiological dynamics of salience, default mode, and frontoparietal networks during episodic memory formation and recall revealed through multi-experiment iEEG replication

Dynamic interactions between large-scale brain networks underpin human cognitive processes, but their electrophysiological mechanisms remain elusive. The triple network model, encompassing the salience network (SN), default mode network (DMN), and frontoparietal network (FPN), provides a framework for understanding these interactions. We analyzed intracranial electroencephalography (EEG) recordings from 177 participants across four diverse episodic memory experiments, each involving encoding as well as recall phases. Phase transfer entropy analysis revealed consistently higher directed information flow from the anterior insula (AI), a key SN node, to both DMN and FPN nodes. This directed influence was significantly stronger during memory tasks compared to resting state, highlighting the AI’s task-specific role in coordinating large-scale network interactions. This pattern persisted across externally driven memory encoding and internally governed free recall. Control analyses using the inferior frontal gyrus (IFG) showed an inverse pattern, with DMN and FPN exerting higher influence on IFG, underscoring the AI’s unique role. We observed task-specific suppression of high-gamma power in the posterior cingulate cortex/precuneus node of the DMN during memory encoding, but not recall. Crucially, these results were replicated across all four experiments spanning verbal and spatial memory domains with high Bayes replication factors. Our findings advance understanding of how coordinated neural network interactions support memory processes, highlighting the AI’s critical role in orchestrating large-scale brain network dynamics during both memory encoding and retrieval. By elucidating the electrophysiological basis of triple network interactions in episodic memory, our study provides insights into neural circuit dynamics underlying memory function and offer a framework for investigating network disruptions in memory-related disorders.

Tutorial on Molecular Latent Space Simulators (LSSs): Spatially and Temporally Continuous Data-Driven Surrogate Dynamical Models of Molecular Systems.

The inherently serial nature and requirement for short integration time steps in the numerical integration of molecular dynamics (MD) calculations place strong limitations on the accessible simulation time scales and statistical uncertainties in sampling slowly relaxing dynamical modes and rare events. Molecular latent space simulators (LSSs) are a data-driven approach to learning a surrogate dynamical model of the molecular system from modest MD training trajectories that can generate synthetic trajectories at a fraction of the computational cost. The training data may comprise single long trajectories or multiple short, discontinuous trajectories collected over, for example, distributed computing resources. Provided the training data provide sufficient sampling of the relevant thermodynamic states and dynamical transitions to robustly learn the underlying microscopic propagator, an LSS furnishes a global model of the dynamics capable of producing temporally and spatially continuous molecular trajectories. Trained LSS models have produced simulation trajectories at up to 6 orders of magnitude lower cost than standard MD to enable dense sampling of molecular phase space and large reduction of the statistical errors in structural, thermodynamic, and kinetic observables. The LSS employs three deep learning architectures to solve three independent learning problems over the training data: (i) an encoding of the high-dimensional MD into a low-dimensional slow latent space using state-free reversible VAMPnets (SRVs), (ii) a propagator of the microscopic dynamics within the low-dimensional latent space using mixture density networks (MDNs), and (iii) a generative decoding of the low-dimensional latent coordinates back to the original high-dimensional molecular configuration space using conditional Wasserstein generative adversarial networks (cWGANs) or denoising diffusion probability models (DDPMs). In this software tutorial, we introduce the mathematical and numerical background and theory of LSS and present example applications of a user-friendly Python package software implementation to alanine dipeptide and a 28-residue beta-beta-alpha (BBA) protein within simple Google Colab notebooks.

Effects of ammonia addition on self-excited Helmholtz and azimuthal thermoacoustic instability modes in a variable-length swirl-stabilized annular combustor

The thermoacoustic instability characteristics in a variable-length annular combustor fueled by ammonia-methane were investigated in the present work, focusing on the effects of ammonia enrichment on various instability modes. A Helmholtz solver was employed to provide a proper estimate of the mode frequencies. The experimental results presented that instability mode changed from the Helmholtz to the first-order azimuthal mode with increasing combustion chamber length. The addition of ammonia significantly weakened the thermoacoustic coupling in both instability modes, mainly in terms of reducing the pressure oscillation amplitudes. A joint analysis of the experimental data and calculated fuel fundamental kinetic properties indicated that ammonia addition attenuated thermoacoustic coupling of the Helmholtz mode by increasing convective time delay and ignition delay time, and weakened the interaction between high-frequency pressure fluctuations and flames in the azimuthal mode by decreasing flame speed. Combined analysis of flame and flow dynamics revealed that for the Helmholtz mode, the flame response to velocity fluctuations was almost symmetric, with the flame exhibiting overall axial motions attributed to pronounced oscillations in both axial velocity and vorticity. Ammonia enrichment changed the location and intensity of interaction regions between neighboring flames. While in the azimuthal mode, the flame response to velocity fluctuations was asymmetric, which was caused by a combination of azimuthal and axial velocity oscillations. The addition of ammonia diminished the asymmetric response of flame and promoted a transition from a dominant azimuthal to a dominant Helmholtz mode during the instability. However, the statistical properties of the modal dynamics in azimuthal instability were not strongly affected by ammonia enrichment.

Modal dynamics of random Raman lasing in multimode graded-index fibers

Modal dynamics of random Raman lasing in multimode graded-index fibers

Fatigue Damage and Reliability Assessment of Wind Turbine Structure During Service Utilizing Real-Time Monitoring Data

Under the action of wind load, a wind turbine tower will produce alternating stress, which leads to fatigue failure. According to the mean wind speed at the wind turbine impeller collected from the SCADA system, the mean wind speed of the simulation point is calculated by using the wind speed exponential model formula. Davenport spectra are used to simulate the pulsating wind speed time series. The wind spectrum is obtained using the harmonic superposition method. Subsequently, the wind speed time series and wind load time series at the simulation point are calculated. Structural modeling of a 5 MW wind turbine tower is performed in ABAQUS 2021. The modal shape and natural frequency are obtained by modal analysis to verify the rationality of the model. Subsequently, wind loads are applied to the model, and structural stress time history is obtained by transient modal dynamics analysis. The stress time history of the maximum stress area of the tower structure is extracted, and the rain flow counting method is applied to it to obtain the stress spectrum. The Weibull distribution of the stress spectrum is fitted, the mean and variance of the total damage in one day are calculated, and the fatigue reliability analysis of the maximum stress area of the tower structure is carried out. And the nonlinear fatigue cumulative damage analysis of the region is carried out. This work has implications for fatigue reliability studies for approximate operating conditions.

Dynamical mode recognition of coupled flame oscillators by supervised and unsupervised learning approaches

Combustion instability in gas turbines and rocket engines, as one of the most challenging problems in combustion research, arises from the complex interactions among flames influenced by chemical reactions, heat and mass transfer, and acoustics. Identifying and understanding combustion instability is essential for ensuring the safe and reliable operation of many combustion systems, where exploring and classifying the dynamical behaviors of complex flame systems is a core task. To facilitate fundamental studies, the present work concerned dynamical mode recognition of coupled flame oscillators made of flickering buoyant diffusion flames, which have gained increasing attention in recent years but are not sufficiently understood. The time series data of flame oscillators were generated through fully validated reacting flow simulations. Due to the limitations of expertise-based models, a data-driven approach was adopted. In this study, a nonlinear dimensional reduction model of variational autoencoder (VAE) was used to project the high dimensional data onto a 2-dimensional latent space. Based on phase trajectories in the latent space, both supervised and unsupervised classifiers were proposed for datasets with and without well-known labeling, respectively. For labeled datasets, we established the Wasserstein-distance-based classifier (WDC) for mode recognition; for unlabeled datasets, we developed a novel unsupervised classifier (GMM-DTW) combining dynamic time warping (DTW) and Gaussian mixture model (GMM). Through comparing with conventional approaches for dimensionality reduction and classification, the proposed supervised and unsupervised VAE-based approaches exhibit a prominent performance across seven assessment metrics for distinguishing dynamical modes, implying their potential extension to dynamical mode recognition in complex combustion problems.

Micromagnetic study of the dynamics of toron chains

This work analyzes the nucleation and stabilization of torons in cylindrical nanopillars with bulk DMI and easy-plane anisotropy. A micromagnetic study reveals the dependence of toron chains on the nanopillar length and the different behaviors when nucleating an even or odd number of torons in a magnetic nanopillar. Spin wave resonant modes in these systems are explored, evidencing differences according to the number of torons. The interplay between torons, chiral bobbers, and their associated spin wave modes is analyzed, which is relevant for applications demanding dynamical modes in the range of gigahertz.

A numerical study on the oscillatory dynamics of tip vortex cavitation

In this paper, we numerically study the mechanism of the oscillatory flow dynamics associated with the tip vortex cavitation (TVC) over an elliptical hydrofoil section. Using our recently developed three-dimensional variational multiphase flow solver, we investigate the TVC phenomenon at Reynolds number $Re = 8.95 \times 10^5$ via dynamic subgrid-scale modelling and the homogeneous mixture theory. To begin, we examine the grid resolution requirements and introduce a length scale considering both the tip vortex strength and the core radius. This length scale is then employed to non-dimensionalize the spatial resolution in the tip vortex region, the results of which serve as a basis for estimation of the required mesh resolution in large eddy simulations of TVC. We next perform simulations to analyse the dynamical modes of tip vortex cavity oscillation at different cavitation numbers, and compare them with the semi-analytical solution. The breathing mode of cavity surface oscillation is extracted from the spatial-temporal evolution of the cavity's effective radius. The temporally averaged effective radius demonstrates that the columnar cavity experiences a growth region followed by decay as it progresses away from the tip. Further examination of the characteristics of local breathing mode oscillations in the growth and decay regions indicates the alteration of the cavity's oscillatory behaviour as it travels from the growth region to the decay region, with the oscillations within the growth region being characterized by lower frequencies. For representative cavitation numbers $\sigma \in [1.2,2.6]$ , we find that pressure fluctuations exhibit a shift of the spectrum towards lower frequencies as the cavitation number decreases, similar to its influence on breathing mode oscillations. The results indicate the existence of correlations between the breathing mode oscillations and the pressure fluctuations. While the low-frequency pressure fluctuations are found to be correlated with the growth region, the breathing mode oscillations within the decay region are related to higher-frequency pressure fluctuations. The proposed mechanism can play an important role in developing mitigation strategies for TVC, which can reduce the underwater radiated noise by marine propellers.

Island change framework defines dominant modes of atoll island dynamics in response to environmental change

Climatic change threatens the persistence of atoll islands and the cultural and ecosystem services they support. However, adaptation and ecosystem management are constrained by lack of knowledge of island-specific transformations. We present an empirically-based island change framework that characterises the physical trajectory of islands, based on high-resolution shoreline analysis on 509 atoll islands in the central Pacific over the past half-century. Using changes in island size and position we identify seven distinct styles of island transformation in the Pacific, including contraction (21.4%), stability (46.1%) and expansion (32.4%), and show that 40% of islands are currently mobile on reef surfaces. Results challenge the framing of islands as erosional, which misrepresents island behaviour and constrains understanding of island futures. The island change framework highlights a broader set of island-specific management considerations, and opportunities, that scale with the style and rate of island change, and provides an empirical basis to inform management.

Robust theory of thermal activation in magnetic systems with Gilbert damping

Magnetic systems can exhibit thermally activated transitions whose timescales are often described by an Arrhenius law. However, robust predictions of such timescales are only available for certain cases. Inspired by the harmonic theory of Langer, we derive a general activation rate for multidimensional spin systems. Assuming local thermal equilibrium in the initial minimum and deriving an expression for the flow of probability density along the unstable dynamical mode at the saddle point, we obtain the expression for the activation rate that is a function of the Gilbert damping parameter, α. We find that this expression remains valid for the physically relevant regime of α≪1. When the activation is characterized by a coherent reorientation of all spins, we gain insight into the prefactor of the Arrhenius law by writing it in terms of spin wave frequencies and, for the case of a finite spin chain, obtain an expression that depends exponentially on the square of the system size indicating a break-down of the Meyer-Neldel rule. Published by the American Physical Society 2024

Pulsational mode dynamics in the presence of negions

The stability dynamics, of the gravito-electrostatically driven pulsational mode of gravitational collapse (PMGC) excitable in spherically symmetric dust molecular clouds (DMCs), is systematically theorized. It actively incorporates the interplay of dust viscosity, dust charge fluctuation dynamics, and negionic (negative ionic) effects. We take the DMC constitutive electrons, positive ions, and negions as the Boltzmann species (lighter). The negatively charged dust grains behave as viscous fluids (heavier). A generalized linear cubic dispersion equation, involving a new and unique set of multi-factorial dispersion coefficients, results from the applied spherical normal mode treatment in the DMC model. The diverse growth characteristics of the PMGC dynamics are investigated illustratively. It is found mainly that the cloud size, dust charge, equilibrium electronic number density, and equilibrium dust number density play stabilizing roles in the PMGC. However, the dust mass, equilibrium positive ionic number density, equilibrium negionic number density, and dust viscosity play destabilizing roles against self-gravity. The reliability of our study is ensured by the fact that the diversified stabilizing features explored here are consistent with the previous results. The applicability of our study in astrophysical gravity-driven structure formation processes moderated actively with the negionic effects is finally outlined.

KdV conformal symmetry breaking in nearly AdS2

We study the gauge theory formulation of Jackiw-Teitelboim gravity and propose Korteweg-de Vries asymptotic conditions that generalize the asymptotic dynamics of the theory. They permit to construct an enlarged set of boundary actions formed by higher order generalizations of the Schwarzian derivative that contain the Schwarzian as lower term in a tower of SL(2, ℝ) invariants. They are extracted from the KdV Hamiltonians and can be obtained recursively. As a result, the conformal symmetry breaking observed in nearly AdS2 is characterized by a much larger set of dynamical modes associated to the stationary KdV hierarchy. We study quantum perturbation theory for the generalized Schwarzian action including the symplectic measure and compute the one-loop correction to the partition function. We find that despite the non-linear nature of the higher-Schwarzian contribution, it acquires a manageable expression that renders a curious leading temperature dependence on the entropy S = #Ta for a an odd integer.

Improving mass lumping and stiffness parameters of bar and hinge model for accurate modal dynamics of origami structures.

The bar and hinge framework uses truss elements and rotational springs to efficiently model the structural behaviour of origami. The framework is especially useful to investigate origami metamaterials as they have repeating geometry, which makes conventional finite element simulations very expensive due to a large number of degrees of freedom. This work proposes improvements to the parameters of bar and hinge model within the context of structural dynamics, specifically modal analysis under small deformations, which has not been carried out previously in the literature. A range of low-frequency modes involving origami folding and panel bending deformations that can be accurately captured by the bar and hinge framework are identified. Within this range, bar and hinge parameters like the lumped masses and the rotational spring stiffness values are derived using conservation laws and finite element tests. The best among the proposed schemes is found to predict natural frequencies of the considered origami structures to within 10% maximum error, improving the accuracy by more than three times from existing schemes. In most cases, the errors in natural frequencies are less than 5%. This article is part of the theme issue 'Origami/Kirigami-inspired structures: from fundamentals to applications'.

Higher-order nonlinear modeling and multi-modal performance of axially restrained diverse flexible shaft-disk assemblies

Flexible shaft-disk combinations are essential in engines, turbines, and machinery. Understanding their modal characteristics is vital for industry-wide performance, reliability, and safety. This study uses higher-order modeling and multi-modal analysis to grasp complex dynamics, especially with multiple disks of varied sizes. Employing Hamilton’s method and Galerkin’s technique, equations of motion are formulated and then utilized for multi-modal analysis considering structural nonlinearity. Various shaft-disk arrangements are analyzed, including disk number, size, position, and shaft material, to understand their impact on modal behavior. The findings reveal the substantial impact of disk count, sizes, and placements on modal dynamics and intricate vibration characteristics across diverse spinning speeds. This effect is accentuated by integrating higher-order modeling into multi-modal analysis. Notably, higher-order deformation enhances overall resilience capacity and imposes more effective vibration restrictions than the linear approach. Furthermore, the investigation explores how different shaft materials influence modal characteristics, critical speeds, and vibration amplitudes in various shaft-disk combinations. Theoretical findings are validated using numerical results from 3D finite element-based models simulated with ANSYS 3D for accuracy. Ultimately, the study introduces a theoretical framework adaptable to numerous flexible shaft-disk combinations, offering a valuable resource for understanding and enhancing system behavior, benefiting engineers and researchers alike.

High-frequency transverse acoustic interactions between two lean-premixed hydrogen combustors connected with cross-fire and cross-talk sections

Large-scale inter-combustor thermoacoustic interactions occur frequently in modern can-annular gas turbine combustion systems due to the presence of an annular cross-talk section upstream of the first stage turbine stator vanes. Although the whole system's modal dynamics depend on the relative dominance of low-frequency planar or high-frequency transverse acoustic waves, the majority of prior studies have chiefly focused on longitudinal mode instabilities. Motivated by the paucity of detailed information, here we examine high-frequency transverse acoustic interactions between two adjacent lean-premixed hydrogen combustors connected via two different pathways, known as cross-fire (XF) and cross-talk (XT) sections. We observe that the dual-combustor system undergoes high-frequency spinning mode in the clockwise direction, manifested as well-defined limit cycles with peak-to-peak amplitude of around 10 kPa. The transverse pressure oscillations, in contrast to the well-established low-frequency mutual synchronization dynamics, are not fully synchronized even in the presence of cross-talk communication. This situation leads to the formation of two closely-spaced 1T modes at 4.76 and 4.86 kHz in the respective hydrogen combustors, accompanied by the beating of the pressure fluctuations solely in the XF tube section. Remarkably, the pressure waves induced near the XF tube section tend to lag behind the pressure waves spinning in the injector face, demonstrating the existence of the angular shift characteristically connected to the high-frequency transverse combustion dynamics.