Dissipative Systems Research Articles

Multiscale Irreversibility Analysis of Time Series Based on Permutation Jensen–Shannon Distance

In this study, we propose a multiscale permutation Jensen–Shannon distance (MPJSD) to measure irreversibility of complex time series. The new quantifier is based on symbolic permutation pattern and Jensen–Shannon distance. As an alternative, the new method offers the best characterization of the underlying irreversibility on different scales. The ARFIMA process and three dissipative chaotic systems are used to verify the effectiveness of the new method. The numerical results indicate that the MPJSD can unveil subtle and interesting findings on different scales and the permutation Jensen–Shannon distance (PJSD) is scale-dependent. Furthermore, we apply the approach to detect the multiscale irreversibility of ECG and financial data. The underlying irreversible nature of the investigated series is well discriminated. The method here introduced gives a new way to distinguish different degrees of irreversibility.

New cases of integrable ninth-order conservative and dissipative dynamical systems

New cases of integrable dynamical systems of the ninth order homogeneous in terms of variables are presented, in which a system on a tangent bundle to a four-dimensional manifold can be distinguished. In this case, the force field is divided into an internal (conservative) and an external one, which has a dissipation of a different sign. The external field is introduced using some unimodular transformation and generalizes the previously considered fields. Complete sets of both first integrals and invariant differential forms are given.

A unified framework for the analysis of accuracy and stability of a class of approximate Gaussian filters for the Navier–Stokes equations

Abstract Bayesian state estimation of a dynamical system utilising a stream of noisy measurements is important in many geophysical and engineering applications. In these cases, nonlinearities, high (or infinite) dimensionality of the state space, and sparse observations pose key challenges for deriving efficient and accurate data assimilation (DA) techniques. A number of DA algorithms used commonly in practice, such as the Ensemble Kalman Filter (EnKF) or Ensemble Square Root Kalman Filter (EnSRKF), suffer from serious drawbacks such as catastrophic filter divergence and filter instability. The analysis of stability and accuracy of these DA schemes has thus far focused either on finite-dimensional dynamics, or on complete observations in case of infinite-dimensional dissipative systems. We develop a unified framework for the analysis of several well-known and empirically efficient DA techniques derived from various Gaussian approximations of the Bayesian filtering schemes for geophysical-type dissipative dynamics with quadratic nonlinearities. We establish rigorous results on (time-asymptotic) accuracy and stability of these algorithms with general covariance and observation operators. The accuracy and stability results for EnKF and EnSRKF for dissipative PDEs are, to the best of our knowledge, completely new in this general setting. It turns out that a hitherto unexploited cancellation property involving the ensemble covariance and observation operators and the concept of covariance localization in conjunction with covariance inflation play a pivotal role in the accuracy and stability for EnKF and EnSRKF. Our approach also elucidates the links, via determining functionals, between the approximate-Bayesian and control-theoretic approaches to DA. We consider the ‘model’ dynamics governed by the two-dimensional incompressible Navier–Stokes equations (NSEs) and observations given by noisy measurements of averaged volume elements or spectral/modal observations of the velocity field. In this setup, several continuous-time DA techniques, namely the so-called 3DVar, EnKF and EnSRKF reduce to a stochastically forced NSEs. For the first time, we derive conditions for accuracy and stability of EnKF and EnSRKF. The derived bounds are given for the limit supremum of the expected value of the L 2 norm and of the H 1 Sobolev norm of the difference between the approximating solution and the actual solution as the time tends to infinity. Moreover, our analysis reveals an interplay between the resolution of the observations (roughly, the ‘richness’ of the observation space) associated with the observation operator underlying the DA algorithms and covariance inflation and localization which are employed in practice for improved filter performance.

A short trajectory is all you need: A transformer-based model for long-time dissipative quantum dynamics.

In this Communication, we demonstrate that a deep artificial neural network based on a transformer architecture with self-attention layers can predict the long-time population dynamics of a quantum system coupled to a dissipative environment provided that the short-time population dynamics of the system is known. The transformer neural network model developed in this work predicts the long-time dynamics of spin-boson model efficiently and very accurately across different regimes, from weak system-bath coupling to strong coupling non-Markovian regimes. Our model is more accurate than classical forecasting models, such as recurrent neural networks, and is comparable to the state-of-the-art models for simulating the dynamics of quantum dissipative systems based on kernel ridge regression.

Optimization of a novel lateral energy dissipation system for cable-stayed bridge with short piers based on seismic vulnerability analysis

Cable-stayed bridges with short piers are susceptible to becoming the structural weak link in seismic resistance during strong earthquakes. Traditional lateral restraint systems, such as fixed and sliding bearing systems, may impose substantial lateral forces and displacement demands, affecting the entire bridge's seismic safety. A butterfly-shaped steel plate damper has been developed and integrated into a lateral energy dissipation system along with elastoplastic cables to address this. Given the significant computational challenges and the complexity of identifying the optimal parameters for the energy dissipation system, this paper combines the probabilistic seismic demand model (PSDM) with the response surface method (RSM) to propose the seismic vulnerability fitting optimization method (SVFOM). Based on SVFOM, utilizing a central composite design (CCD), 17 analysis scenarios that account for the parameter variations of the lateral energy dissipation system are established. By developing response surface functions for bearing damage (ys) and pier damage (yp) through the seismic response fitting of the structure across all scenarios, we determined the optimal parameters for the lateral energy dissipation system to minimize damage to both bearings and piers. The finite element model validation analysis, which demonstrated significant reductions in pier displacement and the probability of bearing damage, confirms the effectiveness of the SVFOM. Optimized parameters showed that the new lateral energy dissipation system can significantly enhance the lateral seismic performance of cable-stayed bridges with short piers. Compared to scenarios without energy dissipation and with lateral sliding bearings, they demonstrated that this optimized system reduces the relative displacement of the piers and girders by 56 % and the probability of complete bearing failure by 87 % while maintaining pier damage within acceptable limits. This optimized system offers a valuable reference for the seismic design of similar bridge structures.

Comparative Analysis of Material Efficiency and the Impact of Perforations on Heat Sinks for Monocrystalline Photovoltaic Panel Cooling

In this research, the design and simulation of a heat sink for photovoltaic panels were carried out using aluminum and copper, the most commonly used materials in heat dissipation systems. This heat sink consisted of fins that were tested both perforated and non-perforated to improve heat dissipation efficiency. This research stems from the need to reduce the temperature of photovoltaic panels during operation, as scientific evidence shows that photovoltaic panels experience a decrease in efficiency as the temperature increases, taking as a reference the temperature under standard test conditions. The simulations of photovoltaic panels with aluminum and copper fins, both perforated and non-perforated, followed a rigorous methodology. For validation, the simulation results were compared with field data, yielding a mean absolute percentage error of 1.71%. The findings indicate that copper fins reduced the temperature of the photovoltaic panel by 2.62 K, resulting in a 1.31% increase in efficiency. Similarly, aluminum fins reduced the temperature by 2.10 K, with a 1.05% increase in efficiency. Perforated copper fins achieved a temperature reduction of 3.07 K, increasing efficiency by 1.54%, while perforated aluminum fins reduced the temperature by 2.49 K, contributing to a 1.25% increase in efficiency.

Dissipative systems have a maximum energy rate density of 105 W/kg

Dissipative systems have a maximum energy rate density of 105 W/kg

Templex-based dynamical units for a taxonomy of chaos.

Discriminating different types of chaos is still a very challenging topic, even for dissipative three-dimensional systems for which the most advanced tool is the template. Nevertheless, getting a template is, by definition, limited to three-dimensional objects based on knot theory. To deal with higher-dimensional chaos, we recently introduced the templex combining a flow-oriented BraMAH cell complex and a directed graph (a digraph). There is no dimensional limitation in the concept of templex. Here, we show that a templex can be automatically reduced into a "minimal" form to provide a comprehensive and synthetic view of the main properties of chaotic attractors. This reduction allows for the development of a taxonomy of chaos in terms of two elementary units: the oscillating unit (O-unit) and the switching unit (S-unit). We apply this approach to various well-known attractors (Rössler, Lorenz, and Burke-Shaw) as well as a non-trivial four-dimensional attractor. A case of toroidal chaos (Deng) is also treated.

Overdamping of vibration resonances by liquid crystal elastomers

This work aims to compare the capability of vibration attenuation by standard elastomeric polymers, and by the new anomalously damping nematic liquid crystal elastomer. We use the most mainstream materials in both categories, and design two testing platforms: the ASTM-standard constrained layer plate resonance geometry, and the attenuation of resonances in a commercial device (electric drill) where the damping polymers were inserted into the casing. In the standard plate resonance testing, we find that LCE outperforms all standard damping materials, moreover, it brings the vibrating plate into the overdamped condition, which is unique for a non-fluid dissipative system. In the attenuation of high-frequency vibrations of a device, we also found LCE dissipates these vibrations much better, although we did not find the optimal insertion configuration for the damping polymer, and did not reach overdamping.

Enhanced superconducting qubit performance through ammonium fluoride etch

Abstract The performance of superconducting qubits is often limited by dissipation and two-level systems (TLS) losses. The dominant sources of these losses are believed to originate from amorphous materials and defects at interfaces and surfaces, likely as a result of fabrication processes or ambient exposure. Here, we explore a novel wet chemical surface treatment at the Josephson junction-substrate and the substrate-air interfaces by replacing a buffered oxide etch (BOE) cleaning process with one that uses hydrofluoric acid followed by aqueous ammonium fluoride. We show that the ammonium fluoride etch process results in a statistically significant improvement in median T1 by ~22% (p=0.002), and a reduction in the number of strongly-coupled TLS in the tunable frequency range. Microwave resonator measurements on samples treated with the ammonium fluoride etch after niobium deposition and etching also show ~33% lower TLS-induced loss tangent compared to the BOE treated samples. As the chemical treatment primarily modifies the Josephson junction-substrate interface and substrate-air interface, we perform targeted chemical and structural characterizations to examine materials differences at these interfaces and identify multiple microscopic changes that could contribute to decreased TLS.

Protein Recruitment to Dynamic DNA-RNA Host Condensates.

We describe the design and characterization of artificial nucleic acid condensates that are engineered to recruit and locally concentrate proteins of interest in vitro. These condensates emerge from the programmed interactions of nanostructured motifs assembling from three DNA strands and one RNA strand that can include an aptamer domain for the recruitment of a target protein. Because condensates are designed to form regardless of the presence of target protein, they function as "host" compartments. As a model protein, we consider Streptavidin (SA) due to its widespread use in binding assays. In addition to demonstrating protein recruitment, we describe two approaches to control the onset of condensation and protein recruitment. The first approach uses UV irradiation, a physical stimulus that bypasses the need for exchanging molecular inputs and is particularly convenient to control condensation in emulsion droplets. The second approach uses RNA transcription, a ubiquitous biochemical reaction that is central to the development of the next generation of living materials. We then show that the combination of RNA transcription and degradation leads to an autonomous dissipative system in which host condensates and protein recruitment occur transiently and that the host condensate size as well as the time scale of the transition can be controlled by the level of RNA-degrading enzyme. We conclude by demonstrating that biotinylated beads can be recruited to SA-host condensates, which may therefore find immediate use for the physical separation of a variety of biotin-tagged components.

Quantum optimal control of the driven dissipative two-level system

Quantum optimal control of the driven dissipative two-level system

Light-Operated Diverse Logic Gates Enabled by Modulating Time-Dependent Fluorescence of Dissipative Self-Assemblies.

Light-fueled dissipative self-assembly possesses enormous potential in the field of optical information due to controllable time-dependent optical signals, but remains a great challenge for constructing intelligent light-operated logic circuits due to the limited availability of optical signal inputs and outputs. Herein, a series of light-fueled dissipative self-assembly systems with variable optical signals are reported to realize diverse logic gates by modulating time-dependent fluorescence variations of the loaded fluorophores. Three kinds of alkyl trimethylammonium homologs are employed to co-assemble with a merocyanine-based photoinduced amphiphile separately to construct a series of dissipative self-assemblies, showing unexpectedly different fluorescence control behaviors of loaded fluorophores during light irradiation and thermal relaxation processes. The opposite monotonicity of time-dependent emission intensity is achieved just by changing the excitation wavelength. Furthermore, by varying the types of trimethylammoniums and excitation wavelengths, a robust logic system is accomplished, integrating AND, XNOR, and XOR functions, which provides an effective pathway for advancing information transmission applications.

Dissipativity of dynamical systems on time scales and the relationship between dissipative differential and dynamical systems

This work is devoted to study the dissipativity property of dynamical systems on time scales and relationship between the dissipativity of systems of dynamic equations on time scales and the corresponding systems of ordinary differential equations. It is established that the dissipativity property is preserved when transitioning from equations on time scales $\mathbb{T}_\lambda$ to the corresponding ordinary differential equations and vice versa, provided that the graininess function $\mu_\lambda$ converges to zero as $\lambda\to0$.

Implementación de Disipador de Energía, TADAS

El Salvador is a country with high seismic demand, so the design and construction processes of buildings today areaimed at reducing the vulnerability caused by earthquakes, as part of the philosophy of damage mitigation and correct structural performance, there are Advances in detailing and construction techniques, so the use of deformation. energy dissipation systems is a safe way to prevent damage at a reasonable economic cost compared to the use of other more complex and sophisticated technologies. In El Salvador, the implementation of the TADAS (Triangular plate Added Damping And Stiffness) energy dissipation system has been implemented in a pioneering way and on a small scale, in the case of a two-story building, structured with Steel frames with ordinary detailing. The first level corresponds to parking, so the TADAS devices have only been installed in a longitudinal direction, while the second level corresponds to various uses of meeting people in meeting rooms and office work, so architectural issue the existence of windows with a view not interrupted by structural elements turned out to be a condition for not installing TADAS on that level. The final result meets the requirements of the ASCE-7-16 standard in deformation control, and prevention of structural damage due to the ductility that TADAS provides to the global structure. All the structural elements satisfy resistance and service requirements given by AISC-ANSI-36016. ILIA: Investigaciones Latinoamericanas en Ingeniería y Arquitectura, No. 01, 2024: 103-107.

Experimental and numerical investigation of a novel passive energy dissipation system with viscoelastic damper and angle-reaction controller

Sharp and significant changes in the relative angle between beam and column during earthquakes often lead to failure of structural joints. This study proposed a novel passive Energy Dissipation system (PEDs) consisting of a viscoelastic damper (VED) and an angle-reaction controller (ARC). The ARC provides mutual support to the joint by establishing temporary supports in reinforced concrete or steel frames to allow for free-angle multilevel control in the case of excessive relative angle. This feature distinguishes the novel PEDs from previous systems as it allows the reaction force of the temporary support to compensate for the loss of joint rotational stiffness. The cyclic loading tests were conducted by constructing fundamental components, and then a mechanical model for the novel PEDs was established. Numerical simulations were performed to analyze parameter variations and to provide a comprehensive methodology for evaluating the seismic performance of the novel PEDs. The results demonstrated that two mechanisms were effectively incorporated into the novel PEDs: vibration energy dissipation and protection against excessive angles. The multistage flag hysteresis curve confirmed the reliability of our theoretical model in representing this novel PEDs. Therefore, supplementing rotational stiffness while achieving energy dissipation can be considered a new approach for enhancing seismic performance in frame structures.

The Sprott B system.

We will consider a thermostatic system, Sprott B, that is a generalization of the well-known one-parameter Sprott A system. Sprott B contains an explicit periodic solution for all positive values of the parameter a. As for Sprott A, we find dissipative KAM tori associated with time-reversal symmetry and canards in dissipative systems. The exact periodic solution is characterized by an infinite number of instability intervals of the parameter. The investigation of the dynamics in these intervals shows the presence of families of stable and unstable periodic solutions, tori, and strange attractors. For large values of the control parameter a, we find non-hyperbolic slow manifolds producing violent vibrations. We discuss a generalization of the Sprott B system with related dynamics.

Exploiting automatic differentiation for quantum optimal control of driven dissipative two-level systems

In principle one can use Pontryagin's minimum principle to treat an optimal control problem and derive the gradient for the cost functional. However, for the driven spin-boson model studied here, the response of the system to the variation of the control is determined by the master equation and the equation of motion for the propagator of the coherent system dynamics. As a result, the application of Pontryagin's minimum principle is less straightforward. An alternative to this approach is the technique of automatic differentiation which in principle amounts to doing calculus on the fully discretized form of the optimal control problem. Automatic differentiation tools can be viewed as black boxes taking as input a program computing the cost functional and giving as output another program computing its gradient. First we derive for the polaron transformed Hamiltonian a Born-Markov master equation using the Bloch-Redfield formalism. By combining the latter with automatic differentiation we are able to implement Z-gate and coherent destruction of tunnelling with high fidelity. Optimization of a dissipative quantum gate poses a more complex numerical problem, since it should occur independent of the input state. To overcome this difficulty, we apply optimal control to the concept of resolvent of the master equation which is the generalisation of quantum unitary evolution operator in the case of dissipative dynamics.

Statistical-Physics-Informed Neural Networks (Stat-PINNs): A machine learning strategy for coarse-graining dissipative dynamics

Machine learning, with its remarkable ability for retrieving information and identifying patterns from data, has emerged as a powerful tool for discovering governing equations. It has been increasingly informed by physics, and more recently by thermodynamics, to further uncover the thermodynamic structure underlying the evolution equations, i.e., the thermodynamic potentials driving the system and the operators governing the kinetics. However, despite its great success, the inverse problem of thermodynamic model discovery from macroscopic data is in many cases non-unique, meaning that multiple pairs of potentials and operators can give rise to the same macroscopic dynamics, which significantly hinders the physical interpretability of the learned models. In this work, we consider the problem of deriving the macroscopic (continuum) equations from microscopic (particle) data, and encode knowledge from statistical mechanics to resolve this non-uniqueness for the first time. The proposed machine learning framework, named as Statistical-Physics-Informed Neural Networks (Stat-PINNs), is here developed for purely dissipative isothermal systems. Interestingly, it only uses data from short-time particle simulations to learn the thermodynamic structure, which can in turn be used to predict long-time macroscopic evolutions. We demonstrate the approach for particle systems with Arrhenius-type interactions, common to a wide range of phenomena, such as defect diffusion in solids, surface absorption, and chemical reactions. Our results from Stat-PINNs can successfully recover the known analytic solution for the case with long-range interactions and discover the hitherto unknown potential and operator governing the short-range interaction cases. We compare our results with direct particle simulations and an analogous approach that solely excludes statistical mechanics, and observe that, in addition to recovering the unique thermodynamic structure, statistical mechanics relations can increase the robustness and predictive capability of the learning strategy.

Present Situation and Future Prospects of Motor Cooling System

Background: The motor cooling system is mainly used in aerospace, automotive, and marine fields, where the motor system is cooled to extend the service life and safety of the motor. The running power of motors rises too fast, which leads to the failure of heat dissipation as expected and shortens the service life of motors. Therefore, it is necessary to choose an appropriate cooling mode for motors to improve their cooling structure, reduce their temperature rise, and improve their reliability and service life. The internal motor cooling system can perform fixed-point heat dissipation, but the overall heat dissipation is poor. The external motor cooling system can perform overall heat dissipation but has high requirements on the structure. Over the years, the application and development of motor cooling systems have gained more and more attention. Objective: This paper discusses the structural characteristics, advantages, and development trends of the motor cooling system in order to reduce the temperature rise of the motor and improve its reliability and service life. This paper aims to provide an overview and systematic guidance for future designs of the motor cooling system. Methods: According to the structural characteristics of the motor cooling system and the requirements of the motor application field, the most typical internal cooling system and external cooling system in the motor cooling system are summarized. Results: By analyzing the causes and hazards of motor heating, the limited requirements of the motor system in use are summarized. The motor system is classified and compared based on the structure, heat dissipation principle, and use of the motor heat dissipation system. The characteristics of small volume and strong heat dissipation ability are also summarized. Based on the analysis of the internal and external cooling system, the problems of complex structure and poor cooling effect are summarized, and the future development trend and direction of the motor cooling system are discussed in detail. Conclusion: This paper divides the motor cooling system into an internal cooling system and an external cooling system according to the specifications and operating environment. This paper expounds on internal and external cooling systems' different principles, advantages, and disadvantages. This paper summarizes the cooling systems in recent years in order to facilitate their subsequent development and use. The main components of additional patents for future inventions are motor cooling systems, innovation in structure simplification, and cost performance optimization.