Energy Dynamics Research Articles

Dynamical dark energy models in the light of gravitational-wave transient catalogues

The study of current gravitational waves (GW) catalogues provide an interesting model independent way to understand further the nature of dark energy. In this work, we present an update of the constrains related to dynamical dark energy parametrisations using recent Gravitational-Wave Transient catalogues (GWTC-1 and GWTC-2) along with Type Ia supernova (SNeIa) and Cosmic Chronometers (CC) catalogues. According to our Bayesian results using the full SNeIa+CC+GW database, the ΛCDM model shows a strong preference against two dark energy parameterisation known as Barboza-Alcaniz (BA) and the Low Correlation (LC) models. Also, we obtain a very strong preference against the Chevallier-Polarski-Linder (CPL) model. Furthermore, we generated a mock GW catalogue and estimate that we require approximately 1000 standard sirens to have a constrain of H 0 within 1% relative error, quantity that is out of reach of current standard sirens candidates in GWTC-1 and GWTC-2 catalogues.

Tidal virialization of dark matter haloes with clustering dark energy

We extend the analysis of Pace et al. [1] by considering the virialization process in the extended spherical collapse model for clustering dark-energy models, i.e., accounting for dark-energy fluctuations. Differently from the standard approach, here virialization is naturally achieved by properly modelling deviations from sphericity due to shear and rotation induced by tidal interactions. We investigate the time evolution of the virial overdensity Δvir in seven clustering dynamical dark energy models and compare the results to the ΛCDM model and to the corresponding smooth dark-energy models.Taking into account all the appropriate corrections, we deduce the abundance of convergence peaks for Rubin Observatory-LSST and Euclid-like weak-lensing surveys, of Sunyaev-Zel'dovich peaks for a Simon Observatory-like CMB survey, and of X-ray peaks for an eROSITA-like survey. Despite the tiny differences in Δvir between clustering and smooth dark-energy models, owing to the large volumes covered by these surveys, five out of seven clustering dark-energy models can be statistically distinguished from ΛCDM. The contribution of dark-energy fluctuation cannot be neglected, especially for the Chevallier-Polarski-Limber and Albrecht-Skordis models, provided the instrumental configurations provide high signal-to-noise ratio. These results are almost independent of the tidal virialization model.

Quintessence and the Swampland: The Numerically Controlled Regime of Moduli Space

AbstractWe provide a detailed discussion of the main theoretical and phenomenological challenges of quintessence model building in any numerically controlled regime of the moduli space of string theory. We argue that a working quintessence model requires a leading order non‐supersymmetric (near) Minkowski vacuum with an axionic flat direction. This axion, when lifted by subdominant non‐perturbative effects, could drive hilltop quintessence only for highly tuned initial conditions and a very low inflationary scale. Our analysis has two important implications. Firstly, scenarios which are in agreement with the swampland conjectures, such as those that include runaways, or supersymmetric AdS and Minkowski vacua, cannot give rise to phenomenologically viable quintessence with full computational control. This raises doubts on the validity of the swampland dS conjecture since it would imply a strong tension between quantum gravity and observations. Secondly, if data should prefer dynamical dark energy, axion models based on alignment mechanisms look more promising than highly contrived hilltop scenarios.

Early evolution constrained by high- p⊥ quark-gluon plasma tomography

We show that high-${p}_{\ensuremath{\perp}}\phantom{\rule{4pt}{0ex}}{R}_{\mathrm{AA}}$ and ${v}_{2}$ are sensitive to the early expansion dynamics, and that the high-${p}_{\ensuremath{\perp}}$ observables prefer delayed onset of energy loss and transverse expansion. To calculate high-${p}_{\ensuremath{\perp}}\phantom{\rule{4pt}{0ex}}{R}_{\mathrm{AA}}$ and ${v}_{2}$, we employ our newly developed DREENA-A framework, which combines state-of-the-art dynamical energy loss model with (3$+$1)-dimensional hydrodynamical simulations. The model applies to both light and heavy flavor, and we predict a larger sensitivity of heavy flavor observables to the onset of transverse expansion. This presents the first time when bulk QGP behavior has been constrained by high-${p}_{\ensuremath{\perp}}$ observables and related theory, i.e., by so-called QGP tomography.

A wave finite element approach for modelling wave transmission through laminated plate junctions

We present a numerical method for computing reflection and transmission coefficients at joints connecting composite laminated plates. The method is based on modelling joints with finite elements with boundary conditions given by the solutions of the wave finite element method for the plates in the infinite half-spaces connected to the joint. There are no restrictions on the number of plates, inter-plate angles, and material parameters of individual layers forming the composite. An L-shaped laminated plate junction is discussed in more detail. Comparisons of numerically predicted scattering coefficients with semi-analytical solutions for the selected structures are presented. The results obtained are essential for statistical energy analysis and dynamical energy analysis based calculations of the wave energy distribution in full built-up structure.

A quintessence dynamical dark energy model from ratio gravity

Based on the work of ratio gravity developed in 2018, which postulates the deformation of the cross ratio to associate with the physical model of gravity, we develop a mechanism to generate dynamical dark energy – a quintessence field coupled with gravity. Such model causes the dark energy behaving differently in early and late time universe. In the radiation-dominated-era and matter-dominated-era, the related analytical solutions of the quintessence field have an interesting property – starting as a constant field, then oscillating as the universe expands. By Markov Chain Monte Carlo search of the parameter space with the local measurement (Type Ia supernovae) in the Bayesian framework, the probed range of H_0 (within 1sigma ) overlaps the H_0 value inferred from Planck CMB dataset by Lambda CDM model.

Constraints from high-precision measurements of the cosmic microwave background: the case of disintegrating dark matter with Λ or dynamical dark energy

In recent years discrepancies have emerged in measurements of the present-day rate of expansion of the universe H 0 and in estimates of the clustering of matter S 8. Using the most recent cosmological observations we reexamine a novel model proposed to address these tensions, in which cold dark matter disintegrates into dark radiation. The disintegration process is controlled by its rate Q = αℋρddm, where α is a (constant) dimensionless parameter quantifying the strength of the disintegration mechanism and ℋ is the conformal Hubble rate in the spatially flat Friedmann-Lemaître-Robertson-Walker universe and ρddm is the energy density of the disintegrating cold dark matter. We constrain this model with the latest 2018 Planck temperature and polarization data, showing that there is no evidence for α≠ 0 and that it cannot solve the H 0 tension below 3σ, clashing with the result obtained by analyzing the Planck 2015 temperature data. We also investigate two possible extensions of the model in which the dark energy equation-of-state parameter w ≠ -1. In this case it is possible to combine Planck data with the SH0ES measurement, and we demonstrate that in both these models the H 0 tension is resolved at the 1σ level, but the condition w ≠ -1 exacerbates the S 8 tension. We also demonstrate that the addition of intermediate-redshift data (from the Pantheon supernova type Ia dataset and baryon acoustic oscillations) weakens the effectiveness of all these models to address the H 0 and S 8 tensions.

Energy Management Strategy of a Novel Electric Dual-Motor Transmission for Heavy Commercial Vehicles Based on APSO Algorithm

With the development of electric vehicles, dual-motor transmission has become a potential alternative for automated manual transmission (AMT) due to the solution of power interruption and the improvement of energy efficiency. In this paper, a novel electric dual-motor transmission (eDMTP) for heavy commercial vehicles is proposed. Then, a 4-layer energy management strategy is developed to optimize dynamics performance and energy efficiency. Subsequently, a real vehicle operation is performed to validate the control strategy and the performance of eDMTP. The results demonstrate that the operating points of the two motors are both in and around the high-efficiency area under normal mode. This research lays the foundation for the development of a pure electric vehicle transmission system.

Localization of plane waves in the stochastic telegrapher's equation.

From the exact solution of the stochastic telegrapher's equation, Fourier plane-wave-like modes are introduced. Then the time evolution of the plane-wave modes are analyzed when the absorption of energy in the telegrapher's equation has strong time fluctuations. We demonstrate that fluctuations in the loss of energy introduce a localized gap with a size that depends on the correlation timescale of the fluctuations. We prove that for a large time correlation the gap is strongly reduced, which means that there is delocalization in the plane-wave modes with respect to the plane waves in the ordinary telegrapher's equation. This result is of relevance in the study of the transport of electromagnetic waves in a conducting medium, and sheds light on the functional role of the fluctuations in the loss of energy in the telegrapher's dynamics.

Nonlinear computational models of dynamical coding patterns in depression and normal rats: from electrophysiology to energy consumption

Major depressive disorder (MDD) is one of the most serious neuropsychiatric disorders. Exploring the pathogenesis and dynamical coding patterns of MDD can provide new targets for clinical drug treatment and new ideas for the research of other neuropsychiatric and neurodegenerative diseases. We selected the medium spiny neuron (MSN) of nucleus accumbens (NAc) as the research objective. NAc is located in the dopaminergic pathway, regulating rewards, emotions and other behaviors. Abnormalities in these behaviors are considered as the main clinical symptoms of MDD. We simulated the different spike patterns of MSNs in MDD group and control group by dynamical Hodgkin–Huxley model. The simulated results can match the electrophysiological experiments, which occurred due to following reasons: (1) The external stimulus current of MDD group was amplified by the local neural microcircuit; (2) the selective permeability to sodium was abnormally decreased; and (3) the dopamine D2 receptor signaling pathway was abnormal in the MDD group. Furthermore, we proposed a dynamical energy model, and the energy results demonstrated that the energy cost in MDD group was lower, which led to persistent depression in patients with MDD. Simultaneously, the negative-to-total energy ratio of MSN in MDD group was higher than that in control group, and the delay time of the power peak and the potential peak in MDD group was shorter than that in the control group. The results showed that the abnormal firing patterns were the direct cause of abnormal behaviors of MDD and indicated that subthreshold activities of MDD group were more intense.

Dynamics analysis and energy consumption modelling based on bond graph: Taking the spindle system as an example

The spindle system of a Computer Numerical Control (CNC) machine tool is essential for improving the machining efficiency and accuracy. Although there are many studies on the energy consumption of machine tool spindle, few comprehensively consider the energy consumption and dynamics characteristics of the mechanical transmission of the spindle system. Therefore, a new bond graph method for modelling the dynamics characteristics and energy consumption of the spindle system of a CNC machine tool is proposed to solve this problem. The bond graph model of the spindle system is established based on the structure and energy flow characteristics of the spindle system. The bond graph model is converted into a dynamic simulation model that can be run in the MATLAB-Simulink environment. The dynamics and energy consumption characteristics of the spindle system are analyzed by observing the response of different input torques in the dynamic simulation model. Finally, the feasibility and practicability of the model are verified by experiments. The predictive accuracy of the proposed model is above 95%, which offers a viable approach for optimizing the design of the spindle system.

Molecular hydrogen isotope separation by a graphdiyne membrane: a quantum-mechanical study.

Graphdiyne (GDY) has emerged as a very promising two-dimensional (2D) membrane for gas separation technologies. One of the most challenging goals is the separation of deuterium (D2) and tritium (T2) from a mixture with the most abundant hydrogen isotope, H2, an achievement that would be of great value for a number of industrial and scientific applications. In this work we study the separation of hydrogen isotopes in their transport through a GDY membrane due to mass-dependent quantum effects that are enhanced by the confinement provided by its intrinsic sub-nanometric pores. A reliable improved Lennard-Jones force field, optimized on accurate ab initio calculations, has been built to describe the molecule-membrane interaction, where the molecule is treated as a pseudoatom. The quantum dynamics of the molecules impacting on the membrane along a complete set of incidence directions have been rigorously addressed by means of wave packet calculations in the 3D space, which have allowed us to obtain transmission probabilities and, in turn, permeances, as the thermal average of the molecular flux per unit pressure. The effect of the different incidence directions on the probabilities is analyzed in detail and it is concluded that restricting the simulations to a perpendicular incidence leads to reasonable results. Moreover, it is found that a simple 1D model-using a zero-point energy-corrected interaction potential-provides an excellent agreement with the 3D probailities for perpendicular incidence conditions. Finally, D2/H2 and T2/H2 selectivities are found to reach maximum values of about 6 and 21 at ≈50 and 45 K, respectively, a feature due to a balance between zero-point energy and tunneling effects in the transport dynamics. Permeances at these temperatures are below recommended values for practical applications, however, at slightly higher temperatures (77 K) they become acceptable while the selectivities preserve promising values, particularly for the separation of tritium.

Dissociative ionization of the H2 molecule under a strong elliptically polarized laser field: carrier-envelope phase and orientation effect.

A coupled electron-nuclear dynamical study is performed to investigate the sub-cycle dissociation and ionization of the H2 molecule in a strong 750 nm 4.5 fs elliptically polarized laser pulse. A quasi-classical method is employed in which additional momentum-dependent potentials are added to the molecular Hamiltonian to account for the non-classical effects. The effect of molecular orientation with respect to the laser polarization plane on the probabilities of different dynamical channels and proton energy spectra has been examined. We demonstrate the 2D-control of proton anisotropy by manipulating the carrier-envelope phase of the pulse. We demonstrate that the quasi-classical method can capture the carrier-envelope phase effects in the dissociative ionization of the H2 molecule. Our results indicate that the classical models provide an efficient approach to study the mechanistic insights of strong-field molecular dynamics.

Prospects for measuring dark energy with 21 cm intensity mapping experiments

Using the 21 cm intensity mapping (IM) technique can efficiently perform large-scale neutral hydrogen (HI) surveys, and this method has great potential for measuring dark-energy parameters. Some 21 cm IM experiments aiming at measuring dark energy in the redshift range of 0<z<3 have been proposed and performed, in which the typical ones using single-dish mode include e.g., BINGO, FAST, and SKA1-MID, and those using interferometric mode include e.g., HIRAX, CHIME, and Tianlai. In this work, we make a forecast for these typical 21 cm IM experiments on their capability of measuring parameters of dark energy. We find that the interferometers have great advantages in constraining cosmological parameters. In particular, the Tianlai cylinder array alone can achieve the standard of precision cosmology for the ΛCDM model (i.e., the precision of parameters is better than 1%). However, for constraining dynamical dark energy, we find that SKA1-MID performs very well. We show that the simulated 21 cm IM data can break the parameter degeneracies inherent in the CMB data, and CMB+SKA1-MID offers σ(w)=0.013 in the wCDM model, and σ(w 0)=0.080 and σ(w a)=0.25 in the CPL model. Compared with CMB+BAO+SN, Tianlai can provide tighter constraints in ΛCDM and wCDM, but looser constraints (tighter than CMB+BAO) in CPL, and the combination CMB+BAO+SN+Tianlai gives σ(w)=0.013, σ(w 0)=0.055, and σ(w a)=0.13. In addition, it is found that the synergy of FAST (0<z<0.35)+SKA1-MID (0.35<z<0.77)+Tianlai (0.77<z<2.55) offers a very promising survey strategy. Finally, we find that the residual foreground contamination amplitude has a considerable impact on constraint results. We show that in the future 21 cm IM experiments will provide a powerful probe for exploring the nature of dark energy.

Space-time statistics of a linear dynamical energy cascade model

&lt;abstract&gt;&lt;p&gt;A linear dynamical model for the development of the turbulent energy cascade was introduced in Apolinário et al. (J. Stat. Phys., &lt;bold&gt;186&lt;/bold&gt;, 15 (2022)). This partial differential equation, randomly stirred by a forcing term which is smooth in space and delta-correlated in time, was shown to converge at infinite time towards a state of finite variance, without the aid of viscosity. Furthermore, the spatial profile of its solution gets rough, with the same regularity as a fractional Gaussian field. We here focus on the temporal behavior and derive explicit asymptotic predictions for the correlation function in time of this solution and observe that their regularity is not influenced by the spatial regularity of the problem, only by the correlation in time of the stirring contribution. We also show that the correlation in time of the solution depends on the position, contrary to its correlation in space at fixed times. We then investigate the influence of a forcing which is correlated in time on the spatial and time statistics of this equation. In this situation, while for small correlation times the homogeneous spatial statistics of the white-in-time case are recovered, for large correlation times homogeneity is broken, and a concentration around the origin of the system is observed in the velocity profiles. In other words, this fractional velocity field is a representation in one-dimension, through a linear dynamical model, of the self-similar velocity fields proposed by Kolmogorov in 1941, but only at fixed times, for a delta-correlated forcing, in which case the spatial statistics is homogeneous and rough, as expected of a turbulent velocity field. The regularity in time of turbulence, however, is not captured by this model.&lt;/p&gt;&lt;/abstract&gt;

Forecasting molecular dynamics energetics of polymers in solution from supervised machine learning.

Machine learning techniques including neural networks are popular tools for chemical, physical and materials applications searching for viable alternative methods in the analysis of structure and energetics of systems ranging from crystals to biomolecules. Efforts are less abundant for prediction of kinetics and dynamics. Here we explore the ability of three well established recurrent neural network architectures for reproducing and forecasting the energetics of a liquid solution of ethyl acetate containing a macromolecular polymer–lipid aggregate at ambient conditions. Data models from three recurrent neural networks, ERNN, LSTM and GRU, are trained and tested on half million points time series of the macromolecular aggregate potential energy and its interaction energy with the solvent obtained from molecular dynamics simulations. Our exhaustive analyses convey that the recurrent neural network architectures investigated generate data models that reproduce excellently the time series although their capability of yielding short or long term energetics forecasts with expected statistical distributions of the time points is limited. We propose an in silico protocol by extracting time patterns of the original series and utilizing these patterns to create an ensemble of artificial network models trained on an ensemble of time series seeded by the additional time patters. The energetics forecast improve, predicting a band of forecasted time series with a spread of values consistent with the molecular dynamics energy fluctuations span. Although the distribution of points from the band of energy forecasts is not optimal, the proposed in silico protocol provides useful estimates of the solvated macromolecular aggregate fate. Given the growing application of artificial networks in materials design, the data-based protocol presented here expands the realm of science areas where supervised machine learning serves as a decision making tool aiding the simulation practitioner to assess when long simulations are worth to be continued.

Computational Fluid Dynamics Simulation and Energy Consumption Analysis of Metal Hydride in Its Hydrogen Charging Process

Computational Fluid Dynamics Simulation and Energy Consumption Analysis of Metal Hydride in Its Hydrogen Charging Process

Accelerating Large-Scale-Structure data analyses by emulating Boltzmann solvers and Lagrangian Perturbation Theory

The linear matter power spectrum is an essential ingredient in all theoretical models for interpreting large-scale-structure observables. Although Boltzmann codes such as CLASS or CAMB are very efficient at computing the linear spectrum, the analysis of data usually requires 104-106 evaluations, which means this task can be the most computationally expensive aspect of data analysis. Here, we address this problem by building a neural network emulator that provides the linear theory (total and cold) matter power spectrum in about one millisecond with ≈0.2%(0.5%) accuracy over redshifts z ≤ 3 (z ≤ 9), and scales10-4 ≤ k [h Mpc-1] &lt; 50. We train this emulator with more than 200,000 measurements, spanning a broad cosmological parameter space that includes massive neutrinos and dynamical dark energy. We show that the parameter range and accuracy of our emulator is enough to get unbiased cosmological constraints in the analysis of a Euclid-like weak lensing survey. Complementing this emulator, we train 15 other emulators for the cross-spectra of various linear fields in Eulerian space, as predicted by 2nd-order Lagrangian Perturbation theory, which can be used to accelerate perturbative bias descriptions of galaxy clustering. Our emulators are specially designed to be used in combination with emulators for the nonlinear matter power spectrum and for baryonic effects, all of which are publicly available at http://www.dipc.org/bacco.

Beyond Thermodynamics: Assessing the Dynamical Softness of Hydrated Ions from First Principles

Ion (de)hydration is a key rate-determining step in interfacial processes from corrosion to electrochemical energy storage. However, predicting the kinetics of ion (de)hydration remains challenging, prompting the use of static proxies such as hydration energy and valence. While useful for assessing thermodynamic preferences, such descriptors cannot fully capture the dynamical softness of the hydration shell that dictates kinetics. Accordingly, we use first-principles molecular dynamics to analyze hydration shell softness for a diverse set of metal cations. Three dynamic metrics are introduced to intuitively describe the bond rigidity, shape deformability, and exchange fluidity of the solvation shell. Together, these metrics capture the relevant physics in the static descriptors, while offering a far more complete and efficient representation for the overall propensity for (de)hydration. Application to the hydrated ion set demonstrates a weak connection between dynamical softness and hydration energy, confirming that dynamical descriptors of hydration are key for correctly describing ion transfer processes.

Exploration of interacting dynamical dark energy model with interaction term including the equation-of-state parameter: alleviation of the H0 tension

We explore a scenario of interacting dynamical dark energy model with the interaction term Q including the varying equation-of-state parameter w. Using the data combination of the cosmic microwave background, the baryon acoustic oscillation, and the type Ia supernovae, to global fit the interacting dynamical dark energy model, we find that adding a factor of the varying w in the function of Q can change correlations between the coupling constant β and other parameters, and then has a huge impact on the fitting result of β. In this model, the fitting value of H 0 is lower at the 3.54σ level than the direct measurement value of H 0. Comparing to the case of interacting dynamical dark energy model with Q excluding w, the model with Q including the constant w is more favored by the current mainstream observation. To obtain higher fitting values of H 0 and narrow the discrepancy of H 0 between different observations, additional parameters including the effective number of relativistic species, the total neutrino mass, and massive sterile neutrinos are considered in the interacting dynamical dark energy cosmology. We find that the H 0 tension can be further reduced in these models, but is still at the about 3σ level.