Energy Fluctuations Research Articles

Achieving chirality and unidirectional emission in optical microcavity via external perturbations

Optical whispering-gallery mode (WGM) microcavities, featured with ultrahigh quality (Q) factors and small mode volumes, are widely investigated in linear optics, nonlinear optics and quantum optics. However, the emission of traditional WGMs is isotropic due to the rotational symmetry of cavity geometries, which hinders their potential in integrated photonic circuits (PCs). Despite the rapid development of microcavity physics, it is essential to explore simple and flexible method to tailor the outputs from the microcavity. In this work, we numerically prove that the regular resonances confined within the stable islands can be affected by purposely manufacturing the energy fluctuation in chaotic sea. By evanescently coupling a waveguide with deformed microcavity, the waveguide induced asymmetric scattering disturbs the balance between the counter-clockwise (CCW) and clockwise (CW) components of the chaotic modes, which sequently brings such inequivalence into stable modes through chaotic-to-regular tunneling. A theoretical model of 4 × 4 non-Hermitian Hamiltonian matrix is employed to illustrate the working principle of the proposed mechanism. Moreover, for the regular modes with angular momentum above critical line, the energy refracting out of the cavity is suppressed, highly improving the collection ratio of waveguide. In this sense, the waveguide simultaneously plays the role of constructing chirality and collecting energy from microcavity. The presented design offers alternative but robust approach to manipulate the chirality of the microcavity. We believe that this research is of great significance to understand the basics and expand the potentials of microcavity.

Investigation of Sub-Grid Scale Turbulence-Radiation Interaction Effects on Turbulence Energy Transport and Varying Thermophysical Properties Using Large Eddy Simulation

Abstract The main objective of this article is to investigate sub-grid scale turbulence–radiation interaction (SGS TRI) effects on SGS turbulence kinetic energy (TKE) fluctuations and varying thermophysical properties in a partially premixed combustion system for a laboratory-piloted methane/air flame. The large eddy simulation approach is employed to simulate the turbulence of the compressible reactive flow. SGS quantities, including turbulent stress and fluxes of enthalpy and species in the sub-grid scale, are computed using the standard Smagorinsky–Lilly model. The radiative transfer equation is modeled by applying the spherical harmonic P1 approximation by considering the radiative heat source related to the SGS TRI contribution. Optically thin fluctuation approximation is utilized to simplify the radiative absorption term. A chemical reaction mechanism comprising 41 steps and 16 species is applied to model methane–air mixture combustion. Diffusion flamelet-generated manifolds are employed to govern the species transport equation. About 87% of TKE is resolved by applying the finest grid consisting of 1,822,580 cells. Impacts of SGS TRI on the spatially filtered density, eddy viscosity, SGS velocity and TKE, overall radiative emission, RMS temperature fluctuations, and nitrogen monoxide (NO) formation are studied. The results reveal that considering SGS TRI in the simulation leads to remarkable discrepancies, particularly in SGS velocity and TKE by 6.70% and 7.40%, respectively. Meanwhile, SGS density and eddy viscosity deviate negligibly in the presence of SGS TRI. Also, the filtered mass fraction of NO reduces up to 17.54% on average by considering TRI.

Performance analysis of a liquid carbon dioxide energy storage system integrated with a coal-fired power plant

The intermittency and fluctuation of renewable energy pose a great threat to the stability of power systems. This adverse effect can be mitigated by using energy storage systems to perform the flexibility transformation of coal-fired power plants (CFPP). In this work, a novel liquid carbon dioxide energy storage (LCES) system integrated with CFPP is proposed. Rather than adopting heat storage tanks, the condensation water from CFPP is drawn to directly retrieve the compression heat during charge, while the high-pressure CO2 at the expander inlet is preheated by the extraction steam during discharge, enabling the LCES system to be thermally decoupled. The thermodynamic models are established, and integration schemes are investigated to obtain better system performance. Thermodynamic analysis reveals that the LCES system can achieve an electricity storage efficiency of 55.04 %, an increase of 8.04 % over the pre-integration level. Meanwhile, the round-trip efficiency, exergy efficiency and energy density of the system are 45.68 %, 57.63 % and 16.01 kWh/m3, respectively. Cold storage and preheaters are the most crucial components for enhancing system performance, according to a detailed exergy analysis. Subsequently, the parametric analysis is conducted to study the effect of some key parameters on the system performance. The variable CFPP load simulation among them shows that CFPP operates more efficiently when it works at low load during charge and at high load during discharge.

A widely applicable and robust LightGBM - Artificial neural network forecasting model for short-term wind power density

Wind energy is a clean and renewable source that reduces greenhouse gas emissions. To smooth the impact of wind energy fluctuations on the power grid and power supply, much research has predicted the wind speed of wind farms to estimate power generation. However, most studies overlook the nonlinear relationship between wind speed and power generation, and data sources are usually limited to one or two wind farms. This study constructs a wind power density prediction model based on the LightGBM and artificial neural network to solve the above problems. Its data collection process does not require meteorological measurement equipment and has good universality, stability, and robustness. LightGBM is used to extract feature information and then train it using an artificial neural network, which has a high tolerance for data loss. The model performance was validated using data from six terrains from 2020 to 2022. While results showed that the average prediction error was 71.68 % less than 2 % and 82.188 % less than 6 %, with an average R2 of 0.9755 and an average correlation coefficient of 0.9875, proving the practical significance of the model that can be used to guide electricity trade.

Modeling and simulation of dynamic characteristics of a green ammonia synthesis system

Green ammonia production system driven by renewable electricity will be under frequent dynamic operation due to the fluctuation of renewable energy. The nonlinear dynamic model of a green ammonia synthesis system is developed and the dynamic responses of PEM (proton exchange membrane) electrolysis subsystem, PSA (Pressure swing adsorption) subsystem and ammonia synthesis subsystem during the start-up process and under the fluctuation of renewable electrical current are analyzed. The start-up dynamic results show that the hydrogen production rate of PEM electrolysis system is positively correlated to the current. The nitrogen output of PSA system is affected by the adsorption time, adsorption pressure, bed length, and air velocity. A start-up period of 300 min from the warmed-up state to the design state can maintain the temperature rise rate of the ammonia synthesis reactor at approximately 40 K/h. The dynamic results under step fluctuations of electrical current show that the PEM electrolysis subsystem and the PSA subsystem response fast compared to the ammonia synthesis reactor and buffer tanks. Using large buffer tanks to smooth the fluctuation allows the system to slide with the variation of renewable current to some extent, resulting in a higher outlet ammonia fraction when the current increases. Different volume ratios of the hydrogen tank to the nitrogen tank volume can lead to different variations of the hydrogen/nitrogen ratio. PID controllers can effectively maintain the bed temperatures, hydrogen/nitrogen ratio, and pressure of the ammonia synthesis reactor at their set points despite fluctuations in renewable electrical current. Buffer tanks are necessary to compensate for the imbalance between the flow rate fluctuation caused by current change and the flow rate regulation caused by PID controllers.

Single frequency pulsed Er,Yb co-doped fiber amplifier with 2 mJ of output energy

High-energy single-frequency pulsed light with low noise is generated from a master oscillator power amplifier (MOPA) system at 1.55 μm eye-safe wavelength using an ultra large mode area Er-Yb gain fiber. At a pulse repetition rate of 2 kHz, the maximum pulse energy without stimulated Brillouin scattering (SBS) reaches 2.07 mJ with 375 ns pulse duration for a pump power of 56.4 W. The pulse energy fluctuation is approximately 0.72 % within 21.5 min. The signal noise ratio (SNR) from the single-frequency fiber amplifier, at the highest pulse energy, centered at 1551.1 nm, is approximately 30 dB. The spectral half width of the signal light is calculated to be 1.28 MHz, which is close to the transform limit.

Optimal scheduling of hydrogen blended integrated electricity–gas system considering gas linepack via a sequential second-order cone programming methodology

In the context of increasing global environmental pollution and constant increase of carbon emission, hydrogen production from surplus renewable energy and hydrogen transportation using existing natural gas pipelines are effective means to mitigate renewable energy fluctuation, build a decarbonized gas network, and achieve the goal of “carbon peak and carbon neutral” in China. Existing gas network modeling with hydrogen blended is divided into steady-state and dynamic. The steady-state modeling oversimplifies the physical process, while the dynamic modeling is computationally intensive. Thus, a quasi-steady-state modeling method of a hydrogen blended integrated electricity–gas system (HBIEGS) considering gas linepack is developed in this paper. The gas linepack refers to the gas stored in natural gas pipelines due to the compressibility of the gas. As a form of gas energy storage, linepack can enhance system flexibility by coping with wind power uncertainty. In the face of nonlinear gas flow equations in HBIEGS, conventional relaxation or approximate methods cannot provide a sufficiently feasible optimal solution. Therefore, a sequential second-order cone programming (S-SOCP) method is proposed to solve the developed model. It can effectively strike a balance between solution speed and the quality of the solution obtained. Finally, the modified IEEE-24-node power system and 12-node natural gas system are used to validate the effectiveness of the developed model and the proposed method. The optimization results show that the proposed method improves the computational efficiency by 91% compared with a general nonlinear solver.

Research on the Hydrodynamic Noise Characteristics of a Mixed-Flow Pump

This study presents a comprehensive investigation of the internal noise characteristics of a mixed-flow pump by combining computational fluid dynamics (CFD) and computational acoustics. The turbulent flow field of the pump is simulated using the unsteady SST k-ω turbulence model in CFD. The contributions of the volute, guide vanes, and impeller to the internal noise are analyzed and compared using the Lighthill theory, FW-H formula, and LMS Virtual Lab software for acoustic simulation. The research findings indicate that the energy of pressure fluctuations in the mixed-flow pump is predominantly concentrated at the blade passing frequency and its low-frequency harmonics. This suggests that the internal noise is mainly in the low-frequency range, with higher energy at the blade passing frequency and its harmonics. Under the 0.6Qdes flow condition, the flow inside the pump becomes more complex, resulting in higher sound pressure levels and sound power levels compared to higher flow conditions. However, for flow conditions ranging from 0.8Qdes to 1.2Qdes, the sound pressure levels gradually increase with increasing flow rate, with the sound pressure level at 1.0Qdes being nearly identical to that at 1.2Qdes. The analysis of sound power level spectra at different flow rates reveals that the distribution characteristics of internal vortex structures directly impact the hydrodynamic noise inside the mixed-flow pump. These research findings provide a significant theoretical basis for noise control in mixed-flow pumps.

Mitochondrial fission drives neuronal metabolic burden to promote stress susceptibility in male mice.

Neurons are particularly susceptible to energy fluctuations in response to stress. Mitochondrial fission is highly regulated to generate ATP via oxidative phosphorylation; however, the role of a regulator of mitochondrial fission in neuronal energy metabolism and synaptic efficacy under chronic stress remains elusive. Here, we show that chronic stress promotes mitochondrial fission in the medial prefrontal cortex via activating dynamin-related protein 1 (Drp1), resulting in mitochondrial dysfunction in male mice. Both pharmacological inhibition and genetic reduction of Drp1 ameliorates the deficit of excitatory synaptic transmission and stress-related depressive-like behavior. In addition, enhancing Drp1 fission promotes stress susceptibility, which is alleviated by coenzyme Q10, which potentiates mitochondrial ATP production. Together, our findings unmask the role of Drp1-dependent mitochondrial fission in the deficits of neuronal metabolic burden and depressive-like behavior and provides medication basis for metabolism-related emotional disorders.

Enhanced control strategy and energy management for a photovoltaic system with hybrid energy storage based on self-adaptive bonobo optimization

Large-scale energy storage systems (ESSs) that can react quickly to energy fluctuations and store excess energy are required to increase the reliability of electricity grids that rely heavily on renewable energy sources (RESs). Hybrid systems, which combine different energy storage technologies such as batteries and supercapacitors, are becoming increasingly popular because no single technology can satisfy all requirements. In this study, a supercapacitor is used to stabilize quickly shifting bursts of power, while a battery is used to stabilize gradually fluctuating power flow. This paper proposes a robust controller for managing the direct current (DC) bus voltage to optimize the performance of ESS. The proposed controller combines a fractional-order proportional integral (FOPI) with a classical PI controller for the first time in the DC microgrid area. The hybrid (FOPI-PI) controller achieves an outstanding and superior performance in all transient and dynamic response specifications compared to other traditional controllers. The parameters of the suggested controller are incorporated with the self-adaptive bonobo optimizer (SaBO) to determine the optimal values. Furthermore, various optimization techniques are applied to the model and the SaBO’s output outperforms other techniques by minimizing the best objective function. In addition, the current study has utilized a novel power management strategy that includes two closed current loops for both batteries and supercapacitors. By using this method, batteries’ lifespans may be increased while still retaining optimal system performance. The suggested controller is implemented in MATLAB/Simulink 2022b, and the outcomes are reported for several case studies. The findings demonstrate that the control technique remarkably improves the transient response, such as transient duration, overshoot/undershoot, and the settling time. The proposed controller (FOPI-PI) with the SaBO optimizer is effective in maintaining the DC bus voltage under load and solar system variation.

How do energy markets react to climate policy uncertainty? Fossil vs. renewable and low-carbon energy assets

This study examines the impact of climate policy uncertainty (CPU) on fossil-based, as well as renewable and low-carbon-based energy markets. Leveraging advanced techniques such as Wavelet Coherence, Quantile Cross-Spectral, and Quantile-on-Quantile analyses, our study uncovers that CPU is a significant driving force for both types of energy assets, though the effect of CPU on energy assets is time-varying. Noticeably, our findings reveal that CPU exerts a substantial adverse effect on most fossil-based energy commodity futures' returns. However, in normal or bearish markets, fossil energy assets exhibit a relatively stronger performance and display favorable associations with CPU at specific frequencies. Overall, fossil energy assets offer limited hedge opportunities against CPU, depending on quantiles and frequencies. Conversely, CPU favors the renewable and low-emission assets' returns, underscoring their resilience and robust hedging characteristics against CPU. These findings hold significant implications for environmentalists, investors, and policymakers, equipping them with valuable insights to make informed decisions and craft appropriate strategies amidst the backdrop of CPU-induced fluctuations in energy markets.

Semiclassical geometry in double-scaled SYK

We argue that at finite energies, double-scaled SYK has a semiclassical approximation controlled by a coupling λ in which all observables are governed by a non-trivial saddle point. The Liouville description of double-scaled SYK suggests that the correlation functions define a geometry in a two-dimensional bulk, with the 2-point function describing the metric. For small coupling, the fluctuations are highly suppressed, and the bulk describes a rigid (A)dS spacetime. As the coupling increases, the fluctuations become stronger. We study the correction to the curvature of the bulk geometry induced by these fluctuations. We find that as we go deeper into the bulk the curvature increases and that the theory eventually becomes strongly coupled. In general, the curvature is related to energy fluctuations in light operators. We also compute the entanglement entropy of partially entangled thermal states in the semiclassical limit.

Can inflation predict energy price volatility?

Fluctuations in energy prices impact production costs and inflation. This study examines whether inflation data can predict volatility in energy markets. Both inflation and energy market volatility exhibit complex behaviour over time, including structural shifts due to demand and supply shocks. Accounting for differences in data frequencies, we use an extended GARCH model (MIDAS) with Laguerre polynomials for time-varying parameters. The empirical results demonstrate that including low-frequency inflation data enhances energy model predictability particularly during periods of high volatility and extreme price fluctuations. Considering inflation improves forecasting for energy market models, benefiting portfolio management and helping policymakers manage inflation.

Exciton-polaron interactions in metal halide perovskite nanocrystals revealed via two-dimensional electronic spectroscopy.

Metal halide perovskite nanocrystals have been under intense investigation for their promise in optoelectronic devices due to their remarkable physics, such as liquid/solid duality. This liquid/solid duality may give rise to their defect tolerance and other such useful properties. This duality means that the electronic states are fluctuating in time, on a distribution of timescales from femtoseconds to picoseconds. Hence, these lattice induced energy fluctuations that are connected to polaron formation are also connected to exciton formation and dynamics. We observe these correlations and dynamics in metal halide perovskite nanocrystals of CsPbI3 and CsPbBr3 using two-dimensional electronic (2DE) spectroscopy, with its unique ability to resolve dynamics in heterogeneously broadened systems. The 2DE spectra immediately reveal a previously unobserved excitonic splitting in these 15nm NCs that may have a coarse excitonic structure. 2D lineshape dynamics reveal a glassy response on the 300fs timescale due to polaron formation. The lighter Br system shows larger amplitude and faster timescale fluctuations that give rise to dynamic line broadening. The 2DE signals enable 1D transient absorption analysis of exciton cooling dynamics. Exciton cooling within this doublet is shown to take place on a slower timescale than within the excitonic continuum. The energy dissipation rates are the same for the I and Br systems for incoherent exciton cooling but are very different for the coherent dynamics that give rise to line broadening. Exciton cooling is shown to take place on the same timescale as polaron formation, revealing both as coupled many-body excitation.

Dynamics of Hindmarsh–Rose neurons connected via adaptive memristive synapse

Experimental data suggest that temporal neural activity regulates the synaptic weight, a phenomenon known as activity-dependent synaptic plasticity. Thus far, a diversity of neuronal learning rules have been adopted; a likely one is a Hebbian adaptation based on neuron synchrony. Regarding the growing appeal for coupling neurons through memristors, an element bridging magnetic flux and ion exchange, the existing literature on adapting memristive synapses seems limited. This paper aims to investigate two Hindmarsh–Rose neurons coupled through an adaptive memristor synapse by treating adaptive coupling parameters as bifurcation parameters. The capability of adaptive memristive coupling to generate high periodic and chaotic oscillations in the intrinsic periodic behavior of HR neurons is revealed. Synchronization analysis suggests that bifurcation scenarios are closely associated with the transition of the generic firings of two neurons from asynchrony to perfect synchrony. The Hamilton energy analysis indicates that as two neurons transition to synchrony, their energy levels equalize, and the energy fluctuations within the memristive coupling channel diminish. The value of the adaption parameter by which neurons synchronize also depends on the intrinsic parameters of HR, particularly the one associated with the effectiveness of slow channels. Additionally, the synchronization's robustness against different noise intensities has been examined. At last, on the matter of multistability, several pieces of evidence also exist.

How fluctuation intensity flux drives SOL expansion

Predictions of heat load widths λq based on particle orbits alone are very pessimistic. This paper shows that pedestal peeling-ballooning (P-B) magnetohydrodynamic (MHD) turbulence broadens the stable scrape-off layer (SOL) by the transport, or spreading, of fluctuation energy from the pedestal. λq is seen to increase with Γε , the fluctuation energy density flux. We elucidate the fundamental physics of the spreading process. Γε increases with pressure fluctuation correlation length. P-B turbulence is seen to be especially effective at spreading, on account of its large effective mixing length. Spreading is shown to be a multiscale process, which is enhanced by the synergy of large and small-scale modes. Pressure fluctuation skewness correlates well with the spreading flux–with the zero crossing of skewness and Γε spatially coincident–suggesting the role of coherent fluctuation structures and the presence of intermittency in λq broadening. λq∼Bp−1 scaling persists for the broadened SOL. We show that the spreading flux increases for increasing pedestal pressure gradient ∇P0 and for decreasing pedestal collisionality υped∗ . This trend is due to the dominance of peeling modes for large ∇P0 and low υped∗ . Ultimately, we see that a state of weak MHD turbulence, as for small ELMs, is very attractive for heat load management. Our findings have transformative implications for future fusion reactor designs and call for experimental investigations to validate the observed trends.

Research on CCHP Design and Optimal Scheduling Based on Concentrating Solar Power, Compressed Air Energy Storage, and Absorption Refrigeration.

In response to the country's "carbon neutrality, peak carbon dioxide emissions" task, this paper constructs an integrated energy system based on clean energy. The system consists of three subsystems: concentrating solar power (CSP), compressed air energy storage (CAES), and absorption refrigeration (AR). Among them, thermal energy storage equipment in the photothermal power generation system can alleviate the fluctuation of solar energy and provide a stable power supply for the system. The compression heat generated during the compression process of the CAES system can be recovered through heat transfer oil to provide a heat load. The compressed air in the air accumulator (ACC) expands in the air turbine to provide an electric load. The low-temperature exhaust gas discharged from the turbine can provide cool load. First, a multienergy system that includes CSP, CAES, and AR is built. Then, the system takes the lowest economic cost as the objective function and constructs the system day-ahead scheduling model. Finally, for data obtained from scene reduction, the commercial optimization software Gurobi is invoked through YALMIP to solve the model. The results show that the three subsystems achieve multienergy complementarity; system operating costs are reduced by 59.94% and fully absorb wind and solar energies by the system.

Properties Underlying the Variation of the Magnetic Field Spectral Index in the Inner Solar Wind

Using data from orbits 1−11 of the Parker Solar Probe mission, the magnetic field spectral index was measured across a range of heliocentric distances. The previously observed transition between a value of −5/3 far from the Sun and a value of −3/2 close to the Sun was recovered, with the transition occurring at around 50 R ⊙ and the index saturating at −3/2 as the Sun is approached. A statistical analysis was performed to separate the variation of the index on distance from its dependence on other parameters of the solar wind that are plausibly responsible for the transition, including the cross helicity, residual energy, turbulence age, and magnitude of magnetic fluctuations. Of all parameters considered, the cross helicity was found to be by far the strongest candidate for the underlying variable responsible. The velocity spectral index was also measured and found to be consistent with −3/2 over the range of values of cross helicity measured. Possible explanations for the behavior of the indices are discussed, including the theorized different behavior of imbalanced, compared to balanced, turbulence.

An IGDT-based a low-carbon dispatch strategy of urban integrated energy system considering intermittent features of renewable energy

The current global energy crisis has prompted a significant rise in the use of low carbon units, making it a crucial research trend in the field of urban energy system, especially in terms of addressing the intermittency of renewable energy generation. This study proposes an integrated energy system planning model (UIES) for urban areas. In addition, to tackle the issue of carbon emissions, a reward and punishment ladder-type carbon transaction model is presented, which aims to control the level of carbon emissions within urban areas. To evaluate the performance of the UIES model, the authors develop an objective function that integrates the sum of equipment operation and maintenance costs, equipment daily acquisition costs, purchased energy costs, carbon trading costs, and integrated demand response (IDR) costs. The stochastic features of renewable energy sources are taken into account in the proposed approach, and an information gap decision theory (IGDT) based strategy is designed to quantify the uncertainty interval and intermittent fluctuations associated with renewable energy. This approach also aims to mitigate any negative effects and variations that may occur during the urban energy system's IDR process. Finally, the simulation tests to demonstrate that the average decrease in operating cost of both models based on IGDT strategy are 7.36 %, and the peak of electrical (EL), heating (HL) and cooling load (CL) are reduced by 20.45 %, 12.38 % and 5.02 % respectively. in conclusion, the energy service providers in urban areas can effectively manage renewable energy fluctuations by independently adjusting the operation sequence of equipment, and the economy and stability of the UIES can be effectively improved by the proposed dispatch strategy.

Heat transfer characteristics of rectangular/triangular/convex-shaped fins filled with hybrid nanoparticles under dry and wet conditions

Numerous researchers have been interested in nanofluids because of their improved thermal characteristics and heat transmission capabilities. Recently, it has been possible to create a novel nanofluid with exceptional thermal properties by combining ternary nanoparticles of various shapes. In this respect, it is believed that the thickness of the fin will change with the length of the fin and that the impacts of thermal radiation, convection on a heat transfer mechanism, and internal heat production in a fin wetted with ternary hybrid nanofluid will depend on the length of the fin. As a result, several fin profiles, including triangular, convex, and rectangular, have been taken into consideration. This study also investigates the comparison of heat and thermal energy fluctuations in both wet and dry conditions. In order to examine the porous nature, Darcy's model is required. With the aid of the Maple computer program, the resultant nonlinear partial differential equation and boundary conditions are non-dimensionalized and numerically resolved using the implicit finite difference approach, the graphic explanation of fin efficiency, and transient thermal response for different values of the essential parameters. The investigation yielded the novel discovery that the effectiveness of the fins is enhanced by the presence of a ternary hybrid nanofluid. Three fins with varied shapes have been compared in both wet and dry circumstances. The study has discovered that triangular fins have a quicker rate of temperature decline, whereas rectangular fins have a greater efficiency. The investigation's results have a significant impact on improving heat transmission in industrial operations.