Physical Systems Research Articles

Optimization of heat transfer in a double lid-driven cavity with isoperimetric heated blocks using GFEM.

This article is concerned with the examination of flow dynamics and heat transfer characteristics in a 1:4 double lid driven cavity in presence of isoperimetric heated blocks of various shapes. The focus is to identify the optimal shape that enhances the heat transfer in a tall cavity. The parametric settings are chosen in such a way that all the convection regimes including natural, forced and mixed convection could be generated. This cavity has lids positioned at the top and bottom, moving in opposite directions along the x-axis. The physical system is represented as a set of coupled partial differential equations incorporating the rheological properties of the power-law fluids (PL). The governing equations in conjunction with various non-dimensional physical parameters are simulated via Galerkin's Finite Element Method (GFEM) on a very fine hybrid grid. The study includes the computation of the Kinetic Energy and Average Nusselt number to determine the optimal shape. It is concluded that the circular block is superior to the other two in terms of heat transmission efficiency.

Protocol for nearly deterministic parity projection on two photonic qubits

Photonic parity projection plays an important role in photonic quantum information processing. Nondestructive parity projections normally require high-fidelity controlled-Z gates between photonic and matter qubits, which can be experimentally demanding. In this paper, we propose a nearly deterministic parity projection protocol on two photonic qubits which only requires stable matter-photon controlled-phase gates. We also demonstrate that our protocol can tolerate moderate Gaussian phase errors in the controlled-phase gates as well as Pauli errors on the matter qubits. The fact that our protocol does not require perfect controlled-Z gates makes it more amenable to experimental implementation. Although we focus on photonic qubits, our protocol can be applied to any physical system or circuit with imperfect controlled-Z gates. Our protocol also provides a new optimization space for parity projection operations on various physical platforms, which is potentially beneficial for achieving high-fidelity parity projection operations. Published by the American Physical Society 2024

Physical reservoir computing: a tutorial

AbstractThis tutorial covers physical reservoir computing from a computer science perspective. It first defines what it means for a physical system to compute, rather than merely evolve under the laws of physics. It describes the underlying computational model, the Echo State Network (ESN), and also some variants designed to make physical implementation easier. It explains why the ESN model is particularly suitable for direct physical implementation. It then discusses the issues around choosing a suitable material substrate, and interfacing the inputs and outputs. It describes how to characterise a physical reservoir in terms of benchmark tasks, and task-independent measures. It covers optimising configuration parameters, exploring the space of potential configurations, and simulating the physical reservoir. It ends with a look at the future of physical reservoir computing as devices get more powerful, and are integrated into larger systems.

Observation of Thouless pumping of light in quasiperiodic photonic crystals

Topological transport is determined by global properties of physical media where it occurs and is characterized by quantized amounts of adiabatically transported quantities. Discovered for periodic potential, it was also explored in disordered and discrete quasiperiodic systems. Here, we report on experimental observation of pumping of a light beam in a genuinely continuous incommensurate photorefractive quasicrystal emulated by its periodic approximants. We observe a universal character of the transport which is determined by the ratio between periods of the constitutive sublattices, by the sliding angle between them, and by Chern numbers of the excited bands (in the time-coordinate space) of the approximant, for which pumping is adiabatic. This reveals that the properties of quasiperiodic systems determining the topological transport are tightly related to those of their periodic approximants and can be observed and studied in a large variety of physical systems. Our results suggest that the links between quasiperiodic systems and their periodic approximants go beyond the pure mathematical relations: They manifest themselves in physical phenomena which can be explored experimentally.

The slow clock is the master

AbstractWe investigate the synchronization and stability of mutually coupled oscillators through small impacts and explore both master–master and master–slave synchrony. The synchronization behaviour between two oscillators, one operating at a frequency $$ \omega $$ ω and the other at a frequency near a multiple of the first one, $$ n \omega $$ n ω where $$ n $$ n is an integer greater than 1, is investigated. It is observed that such systems tend to exhibit a consistent pattern of master–slave relationship. This phenomenon is geometrically elucidated by considering the interaction dynamics: while the multiple bursts of the faster oscillator tend to cancel each other out upon impacting the slower oscillator, the single burst of the slower oscillator on the faster has a secular effect. We propose that this synchronization pattern, arising from perturbative impacts, is prevalent across various physical systems. Further experimental validation of these findings could contribute to a deeper understanding of synchronization phenomena and their implications in natural and technological domains.

How Sophisticated Are Neural Networks Needed to Predict Long-Term Nonadiabatic Dynamics?

Nonadiabatic dynamics is key for understanding solar energy conversion and photochemical processes in condensed phases. This often involves the non-Markovian dynamics of the reduced density matrix in open quantum systems, where knowledge of the system's prior states is necessary to predict its future behavior. In this study, we explore time-series machine learning methods for predicting long-time nonadiabatic dynamics based on short-time input data, comparing these methods with the physics-based transfer tensor method (TTM). To understand the impact of memory time on these approaches, we demonstrate that non-Markovian dynamics can be represented as a linear map within the Nakajima-Zwanzig generalized quantum master equation framework. We further propose a practical method to estimate the effective memory time, within a given tolerance, for reduced density matrix propagation. Our predictive models are applied to various physical systems, including spin-boson models, multistate harmonic (MSH) models with Ohmic spectral densities and for a realistic organic photovoltaic system composed of a carotenoid-porphyrin-fullerene triad dissolved in tetrahydrofuran. Results indicate that the simple linear-mapping fully connected neural network (FCN) outperforms the more complicated nonlinear-mapping networks including the gated recurrent unit (GRU) and the convolutional neural network/long short-term memory (CNN-LSTM) in systems with short memory times, such as spin-boson and MSH models. Conversely, the nonlinear CNN-LSTM and GRU models yield higher accuracy in the triad MSH systems characterized by long memory times. These findings offer valuable insights into the role of effective memory time in non-Markovian quantum dynamics, providing practical guidance for the application of time-series machine learning models to complex chemical systems.

Experimental realization of on-chip few-photon control around exceptional points.

Non-Hermitian physical systems have attracted considerable attention in recent years for their unique properties around exceptional points (EPs), where the eigenvalues and eigenstates of the system coalesce. Phase transitions near exceptional points can lead to various interesting phenomena, such as unidirectional wave transmission. However, most of those studies are in the classical regime and whether these properties can be maintained in the quantum regime is still a subject of ongoing studies. Using a non-Hermitian on-chip superconducting quantum circuit, here we observe a phase transition and the corresponding exceptional point between the two phases. Furthermore, we demonstrate that unidirectional microwave transmission can be achieved even in the few-photon regime within the broken symmetry phase. This result holds some potential applications, such as on-chip few-photon microwave isolators. Our study reveals the possibility of exploring the fundamental physics and practical quantum devices with non-Hermitian systems based on superconducting quantum circuits.

Bloch classification surface for three-band systems

Abstract Topologically protected states can be found in physical systems, that show singularities in some energy contour diagram. These singularities can be characterized by winding numbers, defined on a classification surface, which maps physical state parameters. We have found a classification surface, which applies for three-band hamiltonian systems in the same way than standard Bloch surface does for two-band ones. This generalized Bloch surface is universal in the sense that it classifies a very large class of three-band systems, which we have exhaustively studied, finding specific classification surfaces, applying for each one.

Network-Based Intrusion Detection for Industrial and Robotics Systems: A Comprehensive Survey

In the face of rapidly evolving cyber threats, network-based intrusion detection systems (NIDS) have become critical to the security of industrial and robotic systems. This survey explores the specialized requirements, advancements, and challenges unique to deploying NIDS within these environments, where traditional intrusion detection systems (IDS) often fall short. This paper discusses NIDS methodologies, including machine learning, deep learning, and hybrid systems, which aim to improve detection accuracy, adaptability, and real-time response. Additionally, this paper addresses the complexity of industrial settings, limitations in current datasets, and the cybersecurity needs of cyber–physical Systems (CPS) and Industrial Control Systems (ICS). The survey provides a comprehensive overview of modern approaches and their suitability for industrial applications by reviewing relevant datasets, emerging technologies, and sector-specific challenges. This underscores the importance of innovative solutions, such as federated learning, blockchain, and digital twins, to enhance the security and resilience of NIDS in safeguarding industrial and robotic systems.

Unraveling coupling delays through a transfer entropy analysis in stochastic processes and non-linear systems

Abstract In this work we propose a transfer entropy approach to discern time-delay couplings within non-linear and stochastic coupled systems. We introduce the concept of “time-wise transfer entropy”, which quantifies the reduction in future uncertainty for a process Y by considering the values of processes X and Y at a specific past moment. The key advantage of our approach is a reduction in the number of parameters required for estimation when compared to other transfer entropy methodologies. Our proposed time-wise transfer entropy not only lends itself to effective estimation from actual data but also enhances our understanding of the origins of seemingly “spurious” couplings observed in some transfer entropy approaches. To validate our method, we apply it to determine coupling delays in minimal stochastic models where the time-wise transfer entropy can be precisely derived in terms of the Shannon entropy. We further assess the technique performance in coupled non-linear systems with delays, demonstrating its capacity to accurately reproduce the corresponding coupling delays. The developed technique may be useful in the analysis of multifactor nonlinear physical systems where complex causal relationships may exist.

The Generative Generic-Field Design Method Based on Design Cognition and Knowledge Reasoning

Large language model (LLM) and Crowd Intelligent Innovation (CII) are reshaping the field of engineering design and becoming a new design context. Generative generic-field design can solve more general design problems innovatively by integrating multi-domain design knowledge. However, there is a lack of knowledge representation and design process model in line with the design cognition of the new context. It is urgent to develop generative generic-field design methods to improve the feasibility, innovation, and empathy of design results. This study proposes a method based on design cognition and knowledge reasoning. Firstly, through the problem formulation, a generative universal domain design framework and knowledge base are constructed. Secondly, the knowledge-based discrete physical structure set generation method and system architecture generation method are proposed. Finally, the application tool Intelligent Design Assistant (IDA) is developed, verified, and discussed through an engineering design case. According to the design results and discussion, the design scheme is feasible and reflects empathy for the fuzzy original design requirements. Therefore, the method proposed in this paper is an effective technical scheme of generative generic-field engineering design in line with the design cognition in the new context.

Spectral Signatures of Nontrivial Topology in a Superconducting Circuit

Topology, like symmetry, is a fundamental concept in understanding general properties of physical systems. In condensed matter, nontrivial topology may manifest itself as singular features in the energy spectrum or the quantization of electrical properties such as conductance and magnetic flux. Using microwave spectroscopy, we determine that a superconducting circuit with three Josephson tunnel junctions in parallel can possess degeneracies indicative of nontrivial topology. We identify three topological invariants, one of which is related to a hidden quantum mechanical supersymmetry. Measurements show that devices fabricated in different topological regimes fall on a simple phase diagram, which should be robust to junction imperfections and geometric inductance. Josephson tunnel junction circuits, which are readily fabricated with conventional microlithography techniques, allow access to a wide range of topological systems that may have no condensed matter analog. Notable spectral features of these circuits, such as degeneracies and flat bands, may find use in quantum information, sensing, and metrology. Published by the American Physical Society 2024

The comparative role of physical system processes in Hudson Strait ice stream cycling: a comprehensive model-based test of Heinrich event hypotheses

Abstract. Despite their recognized significance on global climate and extensive research efforts, the mechanism(s) driving Heinrich events remain(s) a subject of debate. Here, we use the 3D thermomechanically coupled glacial systems model (GSM) to examine the Hudson Strait ice stream surge cycling and the role of three factors previously hypothesized to play a critical role in Heinrich events: ice shelves, glacial isostatic adjustment, and sub-surface ocean temperature forcings. In contrast to all previous modeling studies examining HEs, the GSM uses a transient last glacial cycle climate forcing, global viscoelastic glacial isostatic adjustment model, and sub-glacial hydrology model. The results presented here are based on a high-variance sub-ensemble retrieved from North American history matching for the last glacial cycle. Over our comparatively wide sampling of the potential parameter space (52 ensemble parameters for climate forcing and process uncertainties), we find two modes of Hudson Strait ice streaming: classic binge–purge versus near-continuous ice streaming with occasional shutdowns and subsequent surge onset overshoot. Our model results indicate that large ice shelves covering the Labrador Sea during the last glacial cycle only occur when extreme calving restrictions are applied. The otherwise minor ice shelves provide insignificant buttressing for the Hudson Strait ice stream. While sub-surface ocean temperature forcing leads to minor differences regarding surge characteristics, glacial isostatic adjustment does have a significant impact. Given input uncertainties, the strongest controls on ice stream surge cycling are the poorly constrained deep geothermal heat flux under Hudson Bay and Hudson Strait and the basal drag law. Decreasing the geothermal heat flux within available constraints and/or using a Coulomb sliding law instead of a Weertman-type power law leads to a shift from the near-continuous streaming mode to the binge–purge mode.

Data-driven inference of high-dimensional spatiotemporal state of plasma systems

Many plasma systems and technologies, such as Hall thrusters for spacecraft propulsion, exhibit complex underlying physics that affect the global operation. When characterizing such systems in an experiment, obtaining full spatiotemporal maps of the involved state variables can be, thus, highly informative. However, this goal is not practically realizable because of various experimental limitations, e.g., finite spatial resolution of the diagnostics and geometrical accessibility constraints. Therefore, having the capability to reconstruct the full high-dimensional states of plasma systems from low-dimensional time-history measurements is greatly desirable. Compressed sensing is a signal processing technique that can answer this crucial need. However, existing compressed sensing approaches have several limitations that restrict their effectiveness for complex physical systems like plasma technologies. These include the need for abundant sensor measurements and a principled sensor placement. In this paper, we demonstrate the capabilities of Shallow Recurrent Decoder (SHRED) architecture for compressed sensing. We show in several plasma test cases that SHRED can robustly infer full high-dimensional spatiotemporal state vectors of these systems (i.e., all macroscopic plasma properties) from minimal system information. This minimal information can consist of three finite time-history measurements of either local values of a plasma property or the global plasma properties (spatially averaged or performance parameters). An application of SHRED's inference capability in the numerical plasma simulation context is “super-resolution” enhancement. We will discuss this application by presenting how SHRED can effectively establish mappings between a low-resolution and a high-resolution simulation, recovering detailed spatial plasma features that are below the simulation's grid size.

Emergent Dynamic Formation through Optical Interactions in a Robot Swarm

Self‐organized formation is a key direction in swarm robotics. It is still challenging to design local interactions toward desired global formations and even more challenging for dynamic formations in a physical robot swarm system. Herein, a self‐organized method for emergent dynamic circling formation in a robot swarm through optical interactions is proposed. First, this method is quantitatively modeled based on the geometrical relations among robots. This model is further adjusted according to the characteristics of the robot swarm system. To demonstrate the effectiveness of this model, the effects of three key parameters of this model are tested on the size and disorder level of the emergent dynamic circling formation. The experimental results are consistent with the model predictions. Overall, a robot swarm system, in the physical environment, is quantitatively controlled to emerge a dynamic circling formation in this article. This work advances the swarm robotics for quantitatively designing local interactions among robots to reliably emerge dynamic global patterns.

Digital Twin Generalization with Meta and Geometric Deep Learning

Deep digital twins (DDTs) are deep neural networks that encode the behavior of complex physical systems. DDTs are excellent system representations due to their ability to continuously adapt to operational changes and their capability to capture complex relationships between system components and processes that cannot be explicitly modeled. For this challenge, DDTs benefit greatly from recent success in geometric deep learning (GDL) which allows the integration of information from multiple systems based on schematic representations. A major challenge in training DDTs is their dependence on the quality and representativeness of training data, especially under the dynamic conditions typical in prognostics and health management (PHM). Recent developments in differentiable simulation present new opportunities for optimizing the training data representativeness. In this thesis, we propose a novel meta-learning framework that trains DDTs using the output from differentiable simulators. This setup enables active optimization of training data sampling through gradient computation, enhancing training speed, robustness, and data representativeness. We extend this framework to address challenges in multi-system data integration in power grids and fault detection in railway traction networks. By applying our framework, we aim to tackle significant challenges in forecasting, anomaly detection and sensor-fault analysis using advanced data fusion techniques. Our approach promises substantial improvements in DDT robustness and operational efficiency, with its effectiveness to be demonstrated through empirical studies on both simple and complex case studies within the power systems domain.

Architecting a digital twin for wind turbine rotor blade aerodynamic monitoring

Digital twins play an ever-increasing role in maximising the value of measurement and synthetic data by providing real-time monitoring of physical systems, integrating predictive models and creating actionable insights. This paper presents the development and implementation of the Aerosense digital twin for aerodynamic monitoring of wind turbine rotor blades. Employing low-cost, easy-to-install microelectromechanical (MEMS) sensors, the Aerosense system collects aerodynamic and acoustic data from rotor blades. This data is analysed through a cloud-based system that enables real-time analytics and predictive modelling. Our methodological approach frames digital twin development as a systems engineering problem and utilises design patterns, design thinking, and a co-design framework from applied category theory to aid in the development process. The paper details the architecture, deployment, and validation of a ‘Digital Shadow’-type twin with simulation/prediction functionalities. The solution pattern is discussed in terms of its implementation challenges and broader applicability. By providing a practical solution to integrating all the digital twin components into a holistic system, we aim to help wind energy specialists learn how to transform a conceptual idea of a digital twin into a functional implementation for any application.

COMPLIANCE OF EXERCISE IN PATIENTS WITH ANKYLOSING SPONDYLITIS IN LOWER MIDDLE-INCOME COUNTRY

Exercise adherence is crucial for the management of patients with ankylosing spondylitis (AS), particularly in reducing symptoms and improving quality of life. However, barriers to exercise adherence in this patient population remain poorly studied, especially in resource-poor settings. This study focused on assessing exercise adherence among AS patients in Quetta, Pakistan, identifying both challenges and facilitators of adherence. Objective: The objective of this study was to assess exercise adherence among AS patients in Quetta, Pakistan, and explore key factors influencing their adherence to prescribed exercise programs, including barriers such as physical limitations, financial constraints, and social and support systems. Methods: This descriptive cross-sectional study was conducted in several rheumatology outpatient clinics in Quetta from January to July 2024. A purposive sample of 20 AS patients was recruited and a self-administered questionnaire was used to collect clinical and demographic data. Thematic analysis was performed to identify key themes related to exercise adherence, including facilitators and barriers. Results: Of the 20 participants, 60% were male and 40% were female, with a mean age of 45 years. Thematic analysis revealed four key themes: physical barriers (including chronic pain and fatigue), financial challenges (such as not being able to afford a gym membership or transportation), social factors (including stigma and lack of support), and motivational influences. Despite these barriers, social support and personalized exercise prescriptions emerged as key facilitators of adherence. Participants who received personalized exercise programs and education on the benefits of physical activity had higher adherence rates. Conclusion: Exercise adherence among AS patients in Quetta is influenced by a complex interaction of physical, social, and financial factors. Eliminating these barriers through patient-centred strategies, including personalized exercise programs and increased social support, can significantly improve adherence. These findings highlight the need for personalized approaches to promote physical activity among patients with AS, especially in resource-poor settings such as Quetta.

Quantum mechanics and statistical physics: Novel frameworks for enhancing natural language processing

Abstract. This article explores the pioneering application of principles from quantum mechanics and statistical mechanics to the field of natural language processing (NLP). By drawing analogies between physical phenomena such as quantum entanglement, phase transitions, and statistical ensembles, and linguistic concepts like semantic relationships, language use dynamics, and lexical diversity, we offer a novel perspective on language analysis and processing. Quantum linguistic models, leveraging the intricacies of entanglement and quantum probability, provide a framework for understanding complex semantic networks and enhancing computational efficiency through quantum computing. Meanwhile, statistical mechanics inspires models for capturing lexical diversity and understanding the evolution of language patterns, akin to phase transitions in physical systems. This interdisciplinary approach not only deepens our understanding of linguistic phenomena but also introduces advanced mathematical and computational techniques to improve NLP tasks.

Nonlinear waves for a variable-coefficient modified Kadomtsev–Petviashvili system in plasma physics and electrodynamics

In this paper, a variable-coefficient modified Kadomtsev–Petviashvili (vcmKP) system is investigated by modeling the propagation of electromagnetic waves in an isotropic charge-free infinite ferromagnetic thin film and nonlinear waves in plasma physics and electrodynamics. Painlevé analysis is given out, and an auto-Bäcklund transformation is constructed via the truncated Painlevé expansion. Based on the auto-Bäcklund transformation, analytic solutions are given, including the solitonic, periodic and rational solutions. Using the Lie symmetry approach, infinitesimal generators and symmetry groups are presented. With the Lagrangian, the vcmKP equation is shown to be nonlinearly self-adjoint. Moreover, conservation laws for the vcmKP equation are derived by means of a general conservation theorem. Besides, the physical characteristics of the influences of the coefficient parameters on the solutions are discussed graphically. Those solutions have comprehensive implications for the propagation of solitary waves in nonuniform backgrounds.