Energy Fluctuations Research Articles

Low-Power Driving Waveform Design for Improving the Display Effect of Electrophoretic Electronic Paper.

To address the high power consumption associated with image refresh operations in EPDs, this paper proposes a low-power driving waveform that reduces the refresh power of EPDs by lowering the system's peak power. Compared to traditional waveforms, this waveform first activates the particles before erasing them, thus reducing voltage polarity changes. Additionally, it introduces a specific duration of 0 V voltage during the activation phase based on the physical characteristics of the electrophoretic particles to reduce the voltage span. Finally, a particular duration of 0 V voltage is introduced during the erasure phase to minimize the voltage span while ensuring the stability and consistency of the reference gray scale. The experimental results demonstrate that, in standard power tests, the new driving waveform reduces the power fluctuation value by 1.33% and the energy fluctuation value by 37.24% compared to the traditional driving waveform. This reduction in refresh power also mitigates screen flicker and ghosting phenomena.

On flow fluctuations in ruptured and unruptured intracranial aneurysms: resolved numerical study

Flow fluctuations have emerged as a promising hemodynamic metric for understanding of hemodynamics in intracranial aneurysms. Several investigations have reported flow instabilities using numerical tools. In this study, the occurrence of flow fluctuations is investigated using either Newtonian or non-Newtonian fluid models in five patient-specific intracranial aneurysms using high-resolution lattice Boltzmann simulation methods. Flow instabilities are quantified by computing power spectral density, proper orthogonal decomposition, and fluctuating kinetic energy of velocity fluctuations. Our simulations reveal substantial flow instabilities in two of the ruptured aneurysms, where the pulsatile inflow through the neck leads to hydrodynamic instability, particularly around the rupture position, throughout the entire cardiac cycle. In other monitoring points, the flow instability is primarily observed during the deceleration phase; typically, the fluctuations begin just after peak systole, gradually decay, and the flow returns to its original, laminar pulsatile state during diastole. Additionally, we assess the rheological impact on flow dynamics. The disparity between Newtonian and non-Newtonian outcomes remains minimal in unruptured aneurysms, with less than a 5% difference in key metrics. However, in ruptured cases, adopting a non-Newtonian model yields a substantial increase in the fluctuations within the aneurysm sac, with up to a 30% higher fluctuating kinetic energy compared to the Newtonian model. The study highlights the importance of using appropriate high-resolution simulations and non-Newtonian models to capture flow fluctuation characteristics that may be critical for assessing aneurysm rupture risk.

Decentralized optimal voltage control for wind farm with deep learning-based data-driven modeling

The random fluctuations of wind energy and external grid voltage disturbances can both lead to serious voltage fluctuations and voltage deviations in the wind farm (WF). Voltage/reactive power control is an effective way to improve the voltage stability of WF. The existing research is based on complex physical models with prior parameter information, and the accuracy and calculation speed of the WF model are difficult to guarantee. To address this issue, this paper explores a decentralized optimal voltage control method for WF with deep learning-based (DL) data-driven modeling (DL-DOVC). A hybrid convolutional neural network (CNN)-Transformer architecture is proposed to establish the data-driven model of WF, leveraging its enhanced capabilities for extracting time series correlation, to learn complex patterns and dynamics from time series data. Then, we develop a fully decentralized voltage control method for WF to regulate the terminal bus voltage of wind turbines (WTs) within a feasible range. A DL-based data-driven predictive controller is specially designed to solve the established data-driven voltage optimization problem, it eliminates the necessity for frequent manual model maintenance and is suitable for real-time application. Additionally, the difficulty of WF decentralized optimal voltage control arises from the inability of local controllers to fully consider the impact of all non-local WTs on the local WT states. By designing the DL-based auxiliary state-feedback controller, the effects of non-local WTs are implicitly considered in the auxiliary feedback control law. A WF with 32*6.25 MW WTs is used to test the proposed DL-DOVC method. Simulation results show that the proposed DL-based model can efficiently learn and predict the WF dynamics under different operating scenarios. The near-global optimal voltage control performance is achieved only with local measurements by employing the DL-DOVC method.

Hyperuniformity in Ashkin–Teller model

We show that equilibrium systems inddimension that obey the inequalitydν>2,known as Harris criterion, exhibit suppressed energy fluctuation in their critical state. Ashkin-Teller model is an example ind = 2 where the correlation length exponentνvaries continuously with the inter-spin interaction strengthλand exceeds the valued2set by Harris criterion whenλis negative; there, the variance of the subsystem energy across a length scalelvaries asld-αwith hyperuniformity exponentα=2(1-ν-1).Point configurations constructed by assigning unity to the sites which has coarse-grained energy beyond a threshold value also exhibit suppressed number fluctuation and hyperuniformiyty with same exponentα.

Investigation on the differences in unsteady film cooling behaviors of gas turbine blades between mainstream and cooling air pulsations for a cylindrical hole

In practice, film cooling on gas turbine blade inevitably works in an unsteady environment, which is introduced by periodical rotor/stator interaction and unsteady combustion. Previous studies have introduced two methods to simulate the realistic unsteady condition: 1) the pulsation of mainstream velocity; and 2) the pulsation of coolant injection. However, up to this point, the differences in instantaneous film cooling behaviors between these two methods remain unclear. This work presents a series of large eddy simulations to exhibit the unsteady flow and film cooling behaviors under steady and the two unsteady flow conditions. The numerical strategy is validated against our time-resolved experimental data. Time-averaged results show that the difference between the two pulsations is not significant if the averaged blowing ratio remains the same. However, the pulsation type plays a dominant role on the transient mode of film coverage. Under the steady condition, film coverage instability is induced by the unsteady trajectory of near-wall vortex structure; but with pulsed environments, the unsteadiness magnitude increases, and the area with high unsteadiness level enlarges. The pulsation of the mainstream velocity induces a more severe film coverage instability compared to the pulsation of the cooling air injection, because of the higher fluctuation energy of the mainstream bulk. Under mainstream pulsation, the probability distribution of instantaneous cooling effectiveness is the most scattered, and the corresponding fluctuation range is the largest.

The genetic association between bipolar disorder and dementia: a qualitative review.

Bipolar disorder is a chronic disorder characterized by fluctuations in mood state and energy and recurrent episodes of mania/hypomania and depression. Bipolar disorder may be regarded as a neuro-progressive disorder in which repeated mood episodes may lead to cognitive decline and dementia development. In the current review, we employed genome-wide association studies to comprehensively investigate the genetic variants associated with bipolar disorder and dementia. Thirty-nine published manuscripts were identified: 20 on bipolar disorder and 19 on dementia. The results showed that the genes CACNA1C, GABBR2, SCN2A, CTSH, MSRA, and SH3PXD2A were overlapping between patients with bipolar disorder and dementia. In conclusion, the genes CACNA1C, GABBR2, SCN2A, CTSH, MSRA, and SH3PXD2A may be associated with the neuro-progression of bipolar disorder to dementia. Further genetic studies are needed to comprehensively clarify the role of genes in cognitive decline and the development of dementia in patients with bipolar disorder.

Optimizing multi-energy systems with enhanced robust planning for cost-effective and reliable operation

This paper introduces a comprehensive and resilient multi-energy system (MES) designed for independent planning and real-time implementation. A robust daily coordinated planning model is proposed, incorporating adjustable optimization with fundamental operational and uncertainty constraints. The model integrates various energy sources and systems, including photovoltaics, wind turbines, combined heat and power (CHP) units, energy storage system (ESS), electric vehicle (EV), electric boilers, and power-to-gas (P2G) facilities, to manage electricity, natural gas, and heat demands. The objective is to minimize MES operational costs while meeting electricity and heat requirements, considering renewable energy uncertainties. It includes the development of a two-stage flexible robust optimization model that accounts for energy equilibrium, capacity constraints, and demand response mechanisms. The model incorporates price-based demand response with both switchable and interruptible loads, enhancing system controllability and flexibility. Additionally, a scenario generation and reduction technique based on the Kantorovich distance is employed to effectively manage forecast errors and uncertainties. A novel modified Slime Mold Algorithm (SMA) is utilized to solve the optimization problem, demonstrating superior convergence and computational efficiency compared to traditional meta-heuristics. The slime mold algorithm is further enhanced with chaos theory, using a sine map to introduce dynamic exploration capabilities. The findings indicate that the proposed multi-energy system model effectively balances electricity, natural gas, and heat loads while accommodating renewable energy fluctuations. The enhanced slime mold algorithm provides optimal solutions swiftly, ensuring reliable and cost-effective multi-energy system operation.

Insight into properties of sizable glass former from volumetric measurements.

Sizable glass formers feature numerous unique properties and potential applications, but many questions regarding their glass transition dynamics have not been resolved yet. Here, we have analyzed structural relaxation times measured as a function of temperature and pressure in combination with the equation of state obtained from pressure-volume-temperature measurements. Despite evidence from previous dielectric studies indicating a remarkable sensitivity of supercooled dynamics to compression, and contrary to intuition, our results demonstrated the proof for the almost equivalent importance of thermal energy and free volume fluctuations in controlling reorientation dynamics of sizable molecules. The found scaling exponent γ = 3.0 and Ev/Ep ratio of 0.6 were typical for glass-forming materials with relaxation dynamics determined by both effects with a minor advantage of thermal fluctuations involvement. It shows that the high values of key parameters characterizing the sensitivity of the glass transition dynamics to pressure changes, i.e., activation volume ΔV and dTg/dP, are not a valid premise for a remarkable contribution of volume to glass transition dynamics.

Generalized Linear Response Theory for the Full Quantum Work Statistics.

We consider a quantum system driven out of equilibrium via a small Hamiltonian perturbation. Building on the paradigmatic framework of linear response theory (LRT), we derive an expression for the full generating function of the dissipated work. Remarkably, we find that all information about the distribution can be encoded in a single quantity, the standard relaxation function in LRT, thus opening up new ways to use phenomenological models to study nonequilibrium fluctuations in complex quantum systems. Our results establish a number of refined quantum thermodynamic constraints on the work statistics that apply to regimes of perturbative but arbitrarily fast protocols, and do not rely on assumptions such as slow driving or weak coupling. Finally, our approach uncovers a distinctly quantum signature in the work statistics that originates from underlying zero-point energy fluctuations. This causes an increased dispersion of the probability distribution at short driving times, a feature that can be probed in efforts to witness nonclassical effects in quantum thermodynamics.

An optimisation method for planning and operating nearshore island power and natural gas energy systems

As the development and use of offshore islands increase, so do the diversity of their energy needs, the cost of utilising the mainland for resource replenishment, and the instability of island energy systems. To address this challenge, this paper introduces an innovative system integrating fossil and renewable energy tailored for these islands, considering seasonal variations and external energy fluctuations. Leveraging a P-graph, an optimisation framework is designed to assess different optimised solutions. A comprehensive analysis is also performed to compare the solutions, while sensitivity assessments evaluate factors including electricity and gas price fluctuations, renewable energy availability, and natural gas demand. Additionally, the carbon emissions resulting from different power generation scenarios are visually represented and thoroughly analysed. The results support two key findings: 1) the cost of combined energy supply is lowest when wind energy accounts for 59 % of the total energy, mainland grid connection accounts for 3 %, fossil fuel energy accounts for 28 %, and natural gas accounts for 10 % of the energy mix; and 2) the price of electricity and natural gas supplied to the islands and the variability in the energy sources of wind and natural gas can significantly affect the cost of the energy system.

Nonlinear MPPT techniques to control hybrid power systems

In recent years, grid-connected multifunctional photovoltaic (PV) systems have proven to be highly efficient. This system integrates PV panels with a DC–DC boost converter (DC–DC–BC) and the electrical distribution grid (DEG). Linking the PV to the AC-DEG is accomplished through a three-level multifunctional voltage source inverter (MVSI). The DC–DC–BC component in this study is engineered to perform maximum power point tracking (MPPT) irrespective of normal or abnormal conditions. The conventional MPPT technique poses several challenges and constraints on system usage. Hence, the suggestion is to adopt synergetic control (SC) and sliding mode control (SMC) to enhance the MPPT technique's performance within the proposed system framework. Moreover, predictive direct power control is applied to the MVSI-based shunt active power filter, utilizing a phase-locked loop technique to achieve multiple objectives: minimizing energy fluctuations, injecting active power, correcting power factors, compensating reactive power, and mitigating harmonic currents. To implement the proposed system, the MATLAB is used for this purpose, with several tests used to study the behavior of the controls proposed in this work. Numerical results indicate significant reductions in active and reactive power fluctuations, with estimated rates of 38.46% and 15.30%, respectively, compared to traditional strategies. Moreover, the total harmonic distortion (THD) of the source current after filtering is reduced by 31.88% under solar irradiation of G = 1000 Wm2. Before filtering, the THD of current experiences a reduction estimated at 97.65%. These findings underscore the superior performance of the proposed control technique across all evaluated aspects.

Particle motion characteristics of vertical pipe on a the horizontal-vertical pneumatic conveying system installed dune models in the different curvature elbows

To minimize energy consumption in transportation, a dune model is developed and installed in elbow with varying radii of curvature in the horizontal-vertical pneumatic conveying system in this study. The experimental study focuses on the effect of dune model in elbow with different radius of curvature on the pneumatic conveying system in terms of pressure drop, additional pressure drop coefficient, and the power loss coefficient. Furthermore, the particle concentration and velocity distribution are measures by using the electrical capacitance tomography (ECT) and the high-speed particle image velocimetry (PIV) technology. Finally, the particle pulsation velocity is analyzed to reveal the particle motion mechanism of vertical pipe with dune model by using the fourier transform and wavelet transform. The results indicate that the minimum gas conveying velocity is reduced by installing the dune model, with a maximum reduction rate of 10.36 %, and the maximum reduction rate of power loss coefficient decreased is 11.56 %. At the same time, the particle concentration near the outside and inside wall of the pipe with dune is lower and higher than of without dune, and the particle axial velocities with dune are larger than the case of no dune, and the particle axial pulsation intensity with dune are larger than the case of no dune. Otherwise, the peak value of power spectrum of with dune is lower than that of no dune model in the low frequency region, and the wavelet fluctuation energy of low frequency has a great influence on the axial velocity of particles near the inside wall of pipe for the case of dune model.

A new dynamic state estimation method for distribution networks based on modified SVSF considering photovoltaic power prediction

The fluctuations brought by the renewable energy access to the distribution network make it difficult to accurately describe the state space model of the distribution network’s dynamic process, which is the basis of the existing dynamic state estimation methods such as the Kalman filter. The inaccurate state space model directly causes an error of dynamic state estimation results. This paper proposed a new dynamic state estimation method which can mitigates the impact of renewable energy fluctuation by considering PV power prediction in establishing distribution network state space model. Firstly, the proposed method mitigates the impact of renewable energy fluctuation by considering PV power prediction in establishing distribution network state space model. Secondly, SVSF filter is introduced to achieve more accurate estimation under noise. The case study and evaluations are carried out based on MATLAB simulation. The results prove that the smooth variable structure filter with photovoltaic power prediction has a better dynamic state estimation effect under the fluctuation of the distribution network compared with the existing Kalman filter.

Quantifying the randomness and scale invariance of the repeating fast radio bursts

ABSTRACT The statistical properties of energy and waiting time carry essential information about the source of repeating fast radio bursts (FRBs). In this paper, we investigate the randomness of energy and waiting time using four data samples from three extremely active repeating FRBs observed by the Five-hundred-meter Aperture Spherical radio Telescope (FAST). We report the deviation from complete randomness of the burst activity using three statistics, i.e. Hurst exponent, Pincus index, and non-Gaussian probability density distribution of fluctuations. First, the Hurst exponent greater than 0.5 reveals that there is long-term memory in the time series of energy and waiting time. Second, the deviation of the Pincus index from 1.0 manifests that the time series is not completely random. Finally, the fluctuations of energy and waiting time follow the scale-invariant q-Gaussian distribution. All these statistical properties imply that, although the time series of repeating FRBs seems to be irregular, they are not completely random, similar to the features of self-organized criticality.

Generation of input spectrum for electrolysis stack degradation test applied to wind power PEM hydrogen production

Hydrogen production by proton exchange membrane electrolysis has good fluctuation adaptability, making it suitable for hydrogen production by electrolysis in fluctuating power sources such as wind power. However, current research on the durability of proton exchange membrane electrolyzers is insufficient. Studying the typical operating conditions of wind power electrolysis for hydrogen production can provide boundary conditions for performance and degradation tests of electrolysis stacks. In this study, the operating condition spectrum of an electrolysis stack degradation test cycle was proposed. Based on the rate of change of the wind farm output power and the time-averaged peak-valley difference, a fluctuation output power sample set was formed. The characteristic quantities that played an important role in the degradation of the electrolysis stack were selected. Dimensionality reduction of the operating data was performed using principal component analysis. Clustering analysis of the data segments was completed using an improved Gaussian mixture clustering algorithm. Taking the annual output power data of wind farms in Northwest China with a sampling rate of 1 min as an example, the cyclic operating condition spectrum of the proton-exchange membrane electrolysis stack degradation test was constructed. After preliminary simulation analysis, the typical operating condition proposed in this paper effectively reflects the impact of the original curve on the performance degradation of the electrolysis stack. This study provides a method for evaluating the degradation characteristics and system efficiency of an electrolysis stack due to fluctuations in renewable energy.

Brownian Fluctuations of a non-confining potential

Brownian fluctuations arise for any quantity that depends on the stochastic variables of a Brownian particle. In this study, we explore the Brownian fluctuations of a two-dimensional quadratic potential that exhibits two regimes: A confining regime and a non-confining regime. Our analysis focuses on the statistical properties of the potential energy changes relative to an initial setup. We decompose the total potential into two components and thoroughly examine their mean, variance, skewness, and excess kurtosis, as well as their respective distributions. By analyzing these quantities individually, and with relation to the total potential, we gain insights into the nature of the statistical fluctuations of potential energy.

Analysis of the spatial variations in the adoption of solar energy technologies among households in rural and urban areas of Konoin sub-County, Kenya

Globally, the overall demand and cost of energy and particularly, for fossil fuels is on the rise due to increasing population and overall development process. The increasing use of fossil fuels has significant ramifications to the environment, such as increase in carbon emissions. There is need, therefore, to adopt the use of green energy technologies like solar energy so as to minimize the negative effects of fossil fuels on the environment. Despite these, there is paucity of information on factors influencing the spatial variations in the adoption of solar energy technologies among households in the study area, which this study sought to investigate. The study was informed by the Diffusion of Innovation theory and Technology Adoption Model. A a descriptive survey research design was employed. A stratified random sample of 387 households was surveyed and data collected analyzed using descriptive statistics and multiple linear regression. The findings reveal significant spatial variations in solar energy adoption rates. Proximity to renewable energy sources and fluctuations in energy costs positively influenced adoption levels. Also, social factors, including: household size; and community support had a positive influence in adoption of solar energy. Further, economic considerations, such as perceived installation costs and anticipated long-term savings, played a significant role in influencing adoption levels. Moreover, geographic variables, particularly access to areas with abundant sunshine, significantly influenced adoption levels.

Large eddy simulation of flow field and pollutant diffusion around buildings with different thermal stratifications

The main aim of the current article is to explore the influence of buildings on flow field structure in the surrounding area and analyze the dispersion of pollutants using the large eddy simulation (LES) model with varying Richardson numbers (Rib). The research findings indicate that the LES model effectively replicates the effect of buildings on flow field structures and pollutant dispersion, closely aligning with wind tunnel experiments (WTE) results. The impact of various Ribvalues on the flow field structure is not immediately evident within recirculation zone on building sheltered (x/H<1.0). However, the impact of thermal stratification becomes more pronounced farther downwind. In the Rib range of 0.22–0.47, significant changes occur in the vortex center position and length of the cavity region, with Rib=0.3 exhibiting a maximum cavity length of approximately 1.9 H, suggesting a potential critical Rib between 0.22 and 0.3. As Rib increases, the energy fluctuation in the high-frequency range rises, while low-frequency energy fluctuation remains comparatively smaller. Moreover, higher Rib values lead to increased pollutant concentration near the ground on building downwind side, making it more prone to pollutant accumulation at ground level.

Geometrically frustrated systems which are as singles hotter than in company

We show that a set of thermally weakly coupled geometrically frustrated systems (GFSs), each of which is constraint to reside at negative Boltzmann temperatures, is in equilibrium cooler than its constituents. It may even exhibit positive temperatures at low energies. The challenge for the second law of thermodynamics arising from potential heat flow related to the gradient of temperatures between a GFS and its environment is resolved by considering the energy fluctuations above the ground state. They are comprised in the canonical temperature, derived from information theory. Whereas the gradient of Boltzmann temperatures gives the direction of the stochastic drift of the most probable state of a GFS within its environment, the canonical temperature gradient defines that of heat flow.

Energy fluctuation pattern recognition coupled with decomposition-integration: A novel ocean tidal energy forecasting system

Ocean tidal energy is a new energy source which is recognized and utilized earlier by human beings. For the complexity of tidal generation and the singleness of tidal prediction, this paper proposes a multi-factor forecasting system for ocean tidal energy based on energy fluctuation pattern recognition with confluent ReliefF-density-based spatial clustering of applications with noise (ReliefF-DBSCAN), improved symplectic geometric mode decomposition (ISGMD) and convolutional neural network−bidirectional long short-term memory (CNN-BiLSTM) with temporal pattern attention (TPA), named ReliefF-DBSCAN-ISGMD-TPA-CNN-BiLSTM. Precisely, ReliefF-DBSCAN can extract effective features and fluctuation patterns, and reduce the forecasting difficulty. ISGMD significantly improves the endpoint effect and false components of the decomposition results, and heightens the stability and correlation of each component. TPA-CNN-BiLSTM method have strong feature extraction ability, memory ability and recognition ability. In the evaluation stage, this study introduces some indicators such as root mean square error (RMSE) and Diebold Mariano (DM) test to evaluate the forecasting system comprehensively. To highlight the effect of the developed system, use actual data collected from Port SanLuis and Port Chicago as experimental data and compare with five contrastive models. The experimental result shows that the predicted value for the developed system is the closest to the real value, and the RMSE, mean absolute error (MAE) and goodness of fit (R2) of Port Chicago are 0.3152, 0.2900 and 0.9964, respectively, indicating that the prediction ability is better than all contrastive models. In addition, the result proves that the decomposition-integration system is beneficial to improve forecasting accuracy. This study can make a meaningful improvement and innovation for the forecasting of ocean tidal energy.