Energy Fluctuations Research Articles

Fluctuations of the free energy in p-spin SK models on two scales

20 years ago, Bovier, Kurkova, and Löwe (Ann Probab 30(2):605–651, 2002) proved a central limit theorem (CLT) for the fluctuations of the free energy in the p-spin version of the Sherrington–Kirkpatrick model of spin glasses at high temperatures. In this paper we improve their results in two ways. First, we extend the range of temperatures to cover the entire regime where the quenched and annealed free energies are known to coincide. Second, we identify the main source of the fluctuations as a purely coupling dependent term, and we show a further CLT for the deviation of the free energy around this random object.

Plasma plume enhancement of a dual-anode vacuum arc thruster with magnetic nozzle

Vacuum arc thruster (VAT) is a type of pulsed electric propulsion device that generates thrust based on vacuum arc discharges, it has great candidate for micro-newton force applications in orbit. To improve both the thrust and longevity of the VAT, a novel dual-anode structure, comprising a central anode and a ring anode, was developed. We conducted an investigation into the plasma discharge and acceleration process within the influence of a magnetic nozzle. The dual-anode architecture resulted in a reduction in the initial plasma impedance, thereby enhancing ion current and velocity. Analysis of surface parameters during discharge revealed a synergistic mechanism between the two insulator-conducting films, enabling a co-cyclic distribution of energy and resistance fluctuations within the discharge. Consequently, the dual-anode setup demonstrated a lifespan extension of at least twofold. Comparative analyses of arc energy, plasma velocity, ion current, and thrust variations with magnetic field strength were conducted between the dual-anode and single-anode configurations under magnetic nozzle influence. Results showed that the dual-anode structure increased ion current and velocity when subjected to magnetic nozzle influence, resulting in a thrust increase of up to 303%. Additionally, we developed a theoretical model for the diffusion coefficient to elucidate the adaptive splitting phenomenon of the arc within the dual-anode structure under magnetic field influence. This model suggests that the dual-anode structure can achieve a more significant enhancement in beam current from the magnetic nozzle compared to the single-anode configuration.

Differentiated pricing for the retail electricity provider optimizing demand response to renewable energy fluctuations

As renewable energy expands, its fluctuations challenge grid security. Demand response (DR) is a crucial solution for ensuring grid stability. This paper designs a two-stage optimization model for the retail electricity provider (REP), aiming to develop differentiated prices to incentivize multiple types of users in DR. In the first stage, the REP utilizes energy storage to adjust electricity purchases. In the second stage, a bi-layer optimization model is constructed. The REP participates in incentive-based DR and sets differentiated prices for lower-level users. Based on these prices, multiple types of users modify their electricity consumption behavior, aiming to maximize the combined satisfaction with the comfort and economy of electricity consumption. Finally, the Karush-Kuhn-Tucker (KKT) method is used to derive the equilibrium solution between user response quantity and price. The results indicate that (1) Combining energy storage and user-side DR boosts responsiveness by 28.5% compared to storage alone and 23.2% compared to user-side DR alone. (2) Energy storage allows the REP to create more compelling prices for residential users. (3) Differentiated pricing boosts industrial users participating in DR, resulting in over 50% higher response rate than uniform pricing. This study provides policy implications for grid managers to develop effective DR programs.

Optimal capacity and multi-stable flexible operation strategy of green ammonia systems: Adapting to fluctuations in renewable energy

The potential of power-to-ammonia is increasingly recognized as a large-scale renewable electrical energy storage technology in the energy-transition landscape. Unlike conventional, continuously operating Haber-Bosch production processes, power-to-ammonia processes face operational challenges stemming from fluctuations in renewable power supply. Systematic strategies that can adapt to these fluctuations are required to improve the operational flexibility and stability of power-to-ammonia processes for industrial applications. This study proposes a multi-stable flexible ammonia operation strategy, which is practical and feasible in engineering. To maximize net profit, a design scheduling coordination optimization model is established and solved using a two-stage algorithmic framework of particle swarm and mixed integer linear programming. Additionally, three flexible scenarios are designed for the simulation analysis of a grid-connected green ammonia system. Compared with the traditional constant inflexible scenario, the multi-stable flexible scenario increases annual net profit by 5.05 M$ and reduces carbon emission intensity by 1.61 kg CO2/kgNH3. Furthermore, compared with the continuous flexible scenario, the volatility of the ammonia production load is reduced by 79.89 %. The multi-stable flexible operation strategy balances the green ammonia system’s economic, safety, and low-carbon attributes. Sensitivity analysis reveals that wind and photovoltaic complementary power generation reduces green ammonia costs and carbon emissions. When the selling price of ammonia exceeds 550 $/t, ammonia production and sales should be prioritized, but at lower ammonia prices, selling electricity to the grid to optimize economic efficiency is advised. This study provides novel insights into the flexible operation of the power-to-ammonia process and is expected to offer guidance for constructing and operating green ammonia systems.

Null geodesics, QNMs, emission energy and thermal fluctuation of charged T-duality black hole with simple logarithmic correction

This research investigates the properties of charged T-duality black holes within the context of string theory. The investigation focuses on the implications of T-duality, electric charge, and thermodynamic parameters on the curvature and behavior of a black hole. The findings demonstrate that physical variables like μ and H have significant impacts on the position of the event horizon, photon radius as well as shadow radius. The study calculates the values of quasinormal modes and observes through the graphical behavior. Additionally, the paper investigates the entropy and free energies of the system by considering simple logarithmic correction and also demonstrates how the T-duality parameter enhances the thermodynamic stability of the Bardeen black hole geometry. We explore the emission energy of the considered black hole. Overall, this research contributes to our understanding of black holes and their role in the universe within the context of string theory.

Pipeline Leak Detection: A Comprehensive Deep Learning Model Using CWT Image Analysis and an Optimized DBN-GA-LSSVM Framework.

Detecting pipeline leaks is an essential factor in maintaining the integrity of fluid transport systems. This paper introduces an advanced deep learning framework that uses continuous wavelet transform (CWT) images for precise detection of such leaks. Transforming acoustic signals from pipelines under various conditions into CWT scalograms, followed by signal processing by non-local means and adaptive histogram equalization, results in new enhanced leak-induced scalograms (ELIS) that capture detailed energy fluctuations across time-frequency scales. The fundamental approach takes advantage of a deep belief network (DBN) fine-tuned with a genetic algorithm (GA) and unified with a least squares support vector machine (LSSVM) to improve feature extraction and classification accuracy. The DBN-GA framework precisely extracts informative features, while the LSSVM classifier precisely distinguishes between leaky and non-leak conditions. By concentrating solely on the advanced capabilities of ELIS processed through an optimized DBN-GA-LSSVM model, this research achieves high detection accuracy and reliability, making a significant contribution to pipeline monitoring and maintenance. This innovative approach to capturing complex signal patterns can be applied to real-time leak detection and critical infrastructure safety in several industrial applications.

Asymmetry in Polymer-Solvent Interactions Yields Complex Thermoresponsive Behavior.

We introduce a lattice framework that incorporates elements of Flory-Huggins solution theory and the q-state Potts model to study the phase behavior of polymer solutions and single-chain conformational characteristics. Without empirically introducing temperature-dependent interaction parameters, standard Flory-Huggins theory describes systems that are either homogeneous across temperatures or exhibit upper critical solution temperatures. The proposed Flory-Huggins-Potts framework extends these capabilities by predicting lower critical solution temperatures, miscibility loops, and hourglass-shaped spinodal curves. We particularly show that including orientation-dependent interactions, specifically between monomer segments and solvent particles, is alone sufficient to observe such phase behavior. Signatures of emergent phase behavior are found in single-chain Monte Carlo simulations, which display heating- and cooling-induced coil-globule transitions linked to energy fluctuations. The framework also capably describes a range of experimental systems. Importantly, and by contrast to many prior theoretical approaches, the framework does not employ any temperature- or composition-dependent parameters. This work provides new insights regarding the microscopic physics that underpin complex thermoresponsive behavior in polymers.

CarbonScaler: Leveraging Cloud Workload Elasticity for Optimizing Carbon-Efficiency

Due to inherent variations in energy's carbon intensity, temporal shifting has become a key method in reducing the carbon footprint of batch workloads. However, temporally shifting workloads involves searching for periods with lower carbon intensity, which increases the workload's completion time. In this paper, we present CarbonScaler, a new approach that reduces carbon emissions of batch workloads without extending their completion time. Our approach relies on applications' ability to change their compute demand by scaling the workload based on fluctuations in energy's carbon intensity. We present a carbon-aware scheduling algorithm, a Kubernetes-based prototype, and an analytic tool to guide the carbon-efficient deployment of batch applications in the cloud. We evaluate CarbonScaler using real-world applications and show that it can yield 33% carbon savings without extending the completion time and up to 32% extra carbon savings over state-of-the-art suspend-resume policies when completion time is flexible.

Forecasting Hourly Energy Fluctuations Using Recurrent Neural Network (RNN)

Energy forecasting is currently essential due to its various benefits. Energy data analysis for forecasting requires a functional method due to the complexity of the observed data. This forecasting study used the Recurrent Neural Networks (RNN) method. Parameters used include batch size, epoch, hidden layers, loss function, and optimizer obtained from hyperparameter tuning grid search. A comparison of different normalization methods, namely min-max, and z-score, was conducted. Using min-max normalization yielded the best performance with MAPE of 3.9398%, RMSE of 0.0630, and R2 of 0.8996. In testing with z-score normalization, it showed a performance of MAPE of 10.6120%, RMSE of 0.7648, and R2 of 0.4142.

Gated Recurrent Unit (GRU) for Forecasting Hourly Energy Fluctuations

In the current digital era, energy use undeniably supports economic growth, increases social welfare, and encourages technological progress. Energy-related information is often presented in complex time series data, such as energy consumption data per hour or in seasonal patterns. Deep learning models are used to analyze the data. The right choice of normalization method has great potential to improve the performance of deep learning models significantly. Deep learning models generally use several normalization methods, including min-max and z-score. In this research, the deep learning model chosen is Gated Recurrent Unit (GRU) because the computational load on GRU is lighter, so it doesn't require too much memory. In addition, the GRU data is easier to train, so that it can save training time. This research phase adopts the CRISP-DM methodology in data mining as a solution commonly used in business and research. This methodology involves six stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. In this research, the model was obtained using five attribute selection, which applied 2 normalization methods: min-max and z-score. With this normalization, the GRU model produces the best MAPE of 3.9331%, RMSE of 0.9022, and R2 of 0.9022. However, when using z-score normalization, the model performance decreases with MAPE of 10.4332%, RMSE of 0.7602, and R2 of 0.4213. Overall, min-max normalization provides better performance in multivariate time series data analysis.

Energy fluctuations of a Brownian particle freely moving in a liquid

We study the statistical properties of the variation of the kinetic energy of a spherical Brownian particle that freely moves in an incompressible fluid at constant temperature. Based on the underdamped version of the generalized Langevin equation that includes the inertia of both the particle and the displaced fluid, we derive an analytical expression for the probability density function of such a kinetic energy variation during an arbitrary time interval, which exactly amounts to the energy exchanged with the fluid in absence of external forces. We also determine all the moments of this probability distribution, which can be fully expressed in terms of a function that is proportional to the velocity autocorrelation function of the particle. The derived expressions are verified by means of numerical simulations of the stochastic motion of a particle in a viscous liquid with hydrodynamic backflow for representative values of the time-scales of the system. Furthermore, we also investigate the effect of viscoelasticity on the statistics of the kinetic energy variation of the particle, which reveals the existence of three distinct regimes of the energy exchange process depending on the values of the viscoelastic parameters of the fluid.

Dynamic modeling and response characteristics of a solar-driven fuel cell hybrid system based on supercritical CO2 Brayton cycle

This study proposes and evaluates a solar-driven system coupled with a solid oxide fuel cell and the supercritical carbon dioxide cycle, aiming to fully utilize the potential of solar energy while ensuring the safe and stable operation of the energy system. The dynamic mathematical model is established by the lumped parameter method, with a maximum model error of less than 5%. This study analyzes the integrated system based on its harsh off-design conditions, with short-term (in seconds), medium-term (in minutes), and long-term (in hours) dynamic responses. Meanwhile, based on the second law of thermodynamics, a study is conducted on the irreversibility of the operation of heat exchangers. Finally, analyze the mismatch between supply and demand in the time scale caused by solar energy fluctuations. The results show that the system can meet 5 h of hydrogen production work in summer and only 2 and a half hours in winter. The transient entropy generation mainly occurs in the first 5 s, with the highest transient additional entropy generation ratio of 71.7%. The findings of this study provide useful information and a reference for ensuring the safe and flexible operation of renewable power generation systems.

Impact of inflow characteristics on the instantaneous power production using detrended fluctuation analysis

This study investigates the influence of inflow characteristics on wind turbine power production using Detrended Fluctuation Analysis (DFA). The research focuses on extracting indicators for the energy of flow fluctuations at small timescales and their scaling properties. This methodology is demonstrated using one year of 1-second SCADA data from an offshore wind farm. These indicators are then compared with the commonly used Turbulence Intensity (TI). The findings demonstrate that DFA coefficients, specifically the DFA y-intercept and the DFA slope, account for a significantly larger portion of the variability in active power. This research enhances SCADA-based performance analysis.

Research on coordinated control technology of hybrid energy storage under grid-connected/off-grid dual mode

Abstract Aiming at the multi-time scale intermittency of renewable energy in microgrids and frequent power fluctuations will lead to a series of power quality issues. So, it is necessary to propose appropriate strategies for controlling the energy storage systems of microgrids to ensure energy balance. In microgrids, two types of battery energy storage are used to form a hybrid energy storage system, namely, zinc-bromine flow battery for energy storage and lithium titanate battery for energy storage. By analyzing the functionality of various energy storage systems in the grid-connected/off-grid dual style, the operation objectives of the hybrid energy storage system in different modes are proposed. In the meantime, the control strategy is formulated by combining the smoothing fluctuation characteristics of power-type energy storage and the peak load shifting features of energy-type energy storage. Finally, the efficiency of the hybrid energy system control strategy is checked by the simulation software in the connected/off-the-grid mode.

A Comparative Analysis of Scale-invariant Phenomena in Repeating Fast Radio Bursts and Glitching Pulsars

The recent discoveries of a remarkable glitch/antiglitch accompanied by fast radio burst (FRB)-like bursts from the Galactic magnetar SGR J1935+2154 have revealed the physical connection between the two. In this work, we study the statistical properties of radio bursts from the hyperactive repeating source FRB 20201124A and of glitches from the pulsar PSR B1737–30. For FRB 20201124A, we confirm that the probability density functions of fluctuations of energy, peak flux, duration, and waiting time well follow the Tsallis q-Gaussian distribution. The derived q values from q-Gaussian distribution keep approximately steady for different temporal interval scales, which indicates that there is a common scale-invariant structure in repeating FRBs. Similar scale-invariant properties can be found in PSR B1737–30's glitches, implying an underlying association between the origins of repeating FRBs and pulsar glitches. These statistical features can be well understood within the same physical framework of self-organized criticality systems.

Relation between soft tissue energy dissipation and leg stiffness in running at different step frequencies.

This study aims to investigate the relationship between soft tissue energy dissipation and leg stiffness during running. Eight recreational healthy male runners (age: 22.2 ± 1.0 years; height: 1.84 ± 0.03 m; mass: 73.7 ± 5.7 kg) were asked to run at different speeds and step frequencies. Their soft tissue energy dissipation was estimated by the difference between the total mechanical work of the body, measured as the work done to move the body centre of mass relative to the surroundings plus the work to move the limbs relative to the body centre of mass, and lower-limb joint work. A mass-spring model with an actuator was used to analyse the force-length curve of the bouncing mechanism of running. In this way, the stiffness and damping coefficient were assessed at each speed and step frequency. Pearson's correlations were used to describe the relationship between the deviation from the spring-mass model and soft tissue energy fluctuations. The soft tissue dissipation was found to be significantly influenced by step frequency, with both positive and negative work phases decreasing when step frequency increases. Moreover, deviation from a spring-mass model was positively associated with the amount of soft tissue dissipation (r > 0.6). The findings emphasize the substantial role of soft tissues in dissipating or returning energy during running, behaving in a damped-elastic manner. Also, we introduce a novel approach for evaluating the elastic rebound of the body during running. The insights gained may have broad implications for assessing running mechanics, with potential applications in various contexts.

Bringing Statistics to Storylines: Rare Event Sampling for Sudden, Transient Extreme Events

AbstractA leading goal for climate science and weather risk management is to accurately model both the physics and statistics of extreme events. These two goals are fundamentally at odds: the higher a computational model's resolution, the more expensive are the ensembles needed to capture accurate statistics in the tail of the distribution. Here, we focus on events that are localized in space and time, such as heavy precipitation events, which can start suddenly and decay rapidly. We advance a method for sampling such events more efficiently than straightforward climate model simulation. Our method combines elements of two existing approaches: adaptive multilevel splitting (AMS), a rare event algorithm that generates rigorous statistics but fails to enhance the sampling of sudden, transient extremes; and “ensemble boosting,” which generates physically plausible storylines of these events but not their statistics. We modify AMS by splitting trajectories well in advance of the event's onset, following the approach of ensemble boosting. Early splitting requires a rejection step that reduces efficiency, but it is critical for amplifying and diversifying simulated events in tests with the Lorenz‐96 model, for which we demonstrate improved sampling of extreme local energy fluctuations by approximately a factor of 10 relative to direct sampling. Our method is related to previous algorithms, including subset simulation and anticipated AMS, but is distinctly tailored to handle bursting events caused by chaotic traveling waves. Our work makes progress toward the goal of efficiently sampling such transient local extremes in atmospheric models.

Influence of offset blade on pressure fluctuation in a centrifugal pump under different conditions

Abstract In this paper, the pressure fluctuation under different working conditions of hydrofoil blade impeller (AF), offset blade impeller (SP) and offset winglet impeller (DAF) were measured and the influence of different structures on pressure fluctuation was analyzed. The results show that the discrete frequency caused by rotor-stator interaction (RSI) between impeller and tongue is the blade passing frequency (fBPF ) and its harmonic frequency. Pressure fluctuation amplitude of blade passing frequency is obviously suppressed by offset blade. The value of pressure fluctuation of DAF is small near the tongue under small flow rate, and the energy suppression effect is obvious. With the increase of flow rate, the fluctuation energy of DAF increases gradually, and that of SP decreases gradually.

Rindler fluids from gravitational shockwaves

We study a correspondence between gravitational shockwave geometry and its fluid description near a Rindler horizon in Minkowski spacetime. Utilizing the Petrov classification that describes algebraic symmetries for Lorentzian spaces, we establish an explicit mapping between a potential fluid and the shockwave metric perturbation, where the Einstein equation for the shockwave geometry is equivalent to the incompressibility condition of the fluid, augmented by a shockwave source. Then we consider an Ansatz of a stochastic quantum source for the potential fluid, which has the physical interpretation of shockwaves created by vacuum energy fluctuations. Under such circumstance, the Einstein equation, or equivalently, the incompressibility condition for the fluid, becomes a stochastic differential equation. By smearing the quantum source on a stretched horizon in a Lorentz invariant manner with a Planckian width (similarly to the membrane paradigm), we integrate fluctuations near the Rindler horizon to find an accumulated effect of the variance in the round-trip time of a photon traversing the horizon of a causal diamond.

Optimization of the Load Command for a Coal-Fired Power Unit via Particle Swarm Optimization–Long Short-Term Memory Model

This study addresses the challenges faced by coal-fired power plants in adapting to energy fluctuations following the integration of renewable energy sources into the power grid. The flexible operation of thermal power plants has become a focal point in academic research. A numerical model of a coal-fired power plant was developed in this study using the Long Short-Term Memory (LSTM) algorithm and the Particle Swarm Optimization (PSO) algorithm based on actual operation data analysis. The combined PSO-LSTM approach improved the accuracy of the model by optimizing parameters. Validation of the model was performed using a Dymola physical simulation model, demonstrating that the PSO-LSTM coupled numerical model accurately simulates coal-fired power plant operations with a goodness of fit reaching 0.998. Overall system performance for comprehensively evaluating the rate and accuracy of unit operation is proposed. Furthermore, the model’s capability to simulate the load variation process of automatic generation control (AGC) under different load command groups was assessed, aiding in optimizing the best load command group. Optimization experiments show that the performance index of output power is optimal within the experimental range when the set load starts and stops are the same and the power of load command γ = 1.8. Specifically, the 50–75% Turbine Heat Acceptance (THA) load rise process enhanced the overall system performance index by 55.1%, while the 75–50% THA load fall process improved the overall system performance index by 54.2%. These findings highlight the effectiveness of the PSO-LSTM approach in optimizing thermal power plant operations and enhancing system performance under varying load conditions.