Discharge Conditions Research Articles

Effects of Different Spatial Distributions of Vegetation on the Hydraulic Characteristics of Overland Flow

ABSTRACTVegetation plays a crucial role in mitigating and controlling soil erosion caused by overland flow. However, variations in the hydraulic characteristics of overland flow induced by the spatial distribution of vegetation with different row and column spacings are often overlooked in existing literature, potentially leading to significant deviations in predicting these characteristics. In this study, 180 lab‐scale runoff tests were conducted to clarify the hydraulic characteristics of overland flow considering six α (the ratio of the lateral distance of vegetation to stem diameter) levels, six β (the ratio of the slope distance of vegetation to stem diameter) levels, and three slope angles (θ) under five flow discharges (Q) conditions. The results show that the observed flow regime of overland flow belongs to the transition flow regions, shifting from slow to rapid as α and/or β increase. The friction coefficient and the proportion of frictional resistance in the total flow resistance increase with increasing α and β. The local resistance dominates the total flow resistance of bare glass slopes. The local resistance coefficient ξ decreases with increasing α and β, however, it initially increases and then decreases with increasing θ. The impact of β on the local resistance is greater for gentle slopes, whereas the impact of α is more significant for steep slopes. ξ exhibits a negative correlation with Re and the ξ‐Re curves gradually level off as α or β increases, while they become steeper with increasing θ. A prediction model for the total flow resistance was established taking into account the combined effects of Re, α, β and θ, which provides better prediction performance than two other relevant models. The results obtained from this study provide valuable insights into the hydraulic characteristics of overland flow and offer clear guidance for vegetation management in controlling soil erosion on slopes with heterogeneous vegetation coverage.

Applicability assessment of equivalent circuit-thermal coupling models on LiFePO4 batteries operated under wide-temperature and high-rate pulse discharge conditions

In military scenario, high-power lithium iron phosphate (LFP) batteries are frequently used under wide-temperature and high-rate pulse discharge conditions. An accurate electro-thermal coupling model (ETCM) is crucial for the safe operations. Equivalent circuit-thermal coupling model (ECTCM), which combines equivalent circuit model (ECM) and lumped thermal model, is the most widely used type of ETCM in applications. However, the applicability of ECTCM under wide-temperature and high-rate pulse discharge conditions is not clear. To assess the applicability of ECTCM under these special conditions, this study establishes six typical ECTCMs and accurately identify their corresponding model parameters. Then, these models are tested under high-rate pulse discharge conditions from -40oC to 50oC. The results indicate that ECTCMs are effective for pulse discharge at ambient and high temperatures, but not suitable for low-temperature conditions below 0oC. When the temperature is below 0oC, the pulse discharge voltage of the batteries can not be accurately simulated by ECTCMs. This work provides guidance for electro-thermal coupling modeling under high-rate pulse discharge conditions, and also points out the direction for the development of high-precision ETCM capable of handling wide-temperature and high-rate pulse discharge in the future.

The response of the secretary of state and the “supervised discharge” provision of the UK mental health bill 2022: Potential problems and opportunities in the wake of Secretary of State for Justice v MM [2018] UKSC 60

This article considers the legal regulation of discharge conditions that amount to deprivation of liberty (DoL) in the sense of Article 5 of the European Convention on Human Rights following the UK Supreme Court's decision in Secretary of State for Justice v MM in 2018. The 2019 response of the Secretary of State for Justice to the MM judgment and the proposed “Supervised Discharge” provision of the UK 2022 Mental Health Bill are reviewed from a critical perspective with several important problems identified.It is recommended that the advice of the Secretary of State to make use of leave provisions under s17 of the MHA in place of conditional discharge is considered cautiously as this may be liable to future legal challenge. The 2022 Draft Bill is likely to yield an effective solution but it is lacking important provisions to ensure accountability of healthcare providers where Supervised Discharge is authorised, opportunities for therapeutic relaxations of restrictions, and safe systems for the recall and conveyance of patients under the Supervised Discharge regime.

Optimization Model for Climatic Change Impact on the Water Quality of Al-Hilla River, Iraq

Optimization Model for Climatic Change Impact on the Water Quality of Al-Hilla River, Iraq

Influence of River Discharge and Tides on the Salinity Structure at the Magdalena River Estuary

The salinity structure in the Magdalena River estuary results from a massive river discharge and a micro-tidal regime over a narrow-deep channel. Both environmental forcings are analyzed here to assess their effects on the temporal and spatial variability of the estuarine system. We investigated long-term (seasonal to interannual) and short-term (diurnal) changes in salinity structure and circulation through in situ measurements and a 3D hydrodynamic model ensemble (21 model runs). The measurements cover low (below 10th percentile) and mean river discharge conditions. Results show that the Magdalena River estuary (MRE) behaves as a strongly stratified system during the dry season (February to April) and as a vertically homogeneous one during the wet and transitional seasons when the river plume exhibits a lift-off regime at the mouth. A river discharge threshold has been found for the estuary to lift-off regime transition, corresponding to the 30th percentile (5200 m3/s). At the seasonal scale, the salinity intrusion length in the MRE shows more reactivity to river discharge variability than other reported strongly stratified estuaries; it is reflected in the scaling factor n\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n$$\\end{document} in the relationship L∼Q-n\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$L\\sim {Q}^{-n}$$\\end{document}. A n\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n$$\\end{document} value of 3.9 is the best fit for the MRE; nevertheless, it follows the Schijf and Schönfeld theoretical model solution for a prismatic flat configuration. Micro tides modulate the salinity intrusion on the diurnal cycle with a more significant effect on salinity intrusion and water exchange than previously reported, particularly during low discharge conditions. The findings from measurements and model scenarios for the MRE can be broadly applied and stand for an exemplary tropical, micro-tidal, anthropogenic intervened system.

Domain knowledge-guided machine learning framework for state of health estimation in Lithium-ion batteries

Accurate estimation of battery state of health is crucial for effective electric vehicle battery management. Here, we propose five health indicators that can be extracted online from real-world electric vehicle operation and develop a machine learning-based method to estimate the battery state of health. The proposed indicators provide physical insights into the energy and power fade of the battery and enable accurate capacity estimation even with partially missing data. Moreover, they can be computed for portions of the charging profile and real-world driving discharging conditions, facilitating real-time battery degradation estimation. The indicators are computed using experimental data from five cells aged under electric vehicle conditions, and a linear regression model is used to estimate the state of health. The results show that models trained with power autocorrelation and energy-based features achieve capacity estimation with maximum absolute percentage error within 1.5% to 2.5%.

Advanced Monitoring and Real-Time State of Temperature Prediction in Lithium-Ion Cells Under Abusive Discharge Conditions Using Data-Driven Modelling

Advanced Monitoring and Real-Time State of Temperature Prediction in Lithium-Ion Cells Under Abusive Discharge Conditions Using Data-Driven Modelling

Machine‐Learning‐Based Accurate Prediction of Vanadium Redox Flow Battery Temperature Rise Under Different Charge–Discharge Conditions

ABSTRACTAccurate prediction of battery temperature rise is very essential for designing efficient thermal management scheme. In this paper, machine learning (ML)‐based prediction of vanadium redox flow battery (VRFB) thermal behavior during charge–discharge operation has been demonstrated for the first time. Considering different currents with a specified electrolyte flow rate, the temperature of a kW scale VRFB system is studied through experiments. Three different ML algorithms; linear regression (LR), support vector regression (SVR), and extreme gradient boost (XGBoost) have been used for prediction. The training and validation of ML algorithms have been done by the practical dataset of a 1 kW 6 kWh VRFB storage under 40 , 45, 50, and 60 A charge–discharge currents and 10 L min−1 of flow rate. A comparative analysis among ML algorithms is done by performance metrics such as correlation coefficient (R2), mean absolute error (MAE), and root mean square error (RMSE). XGBoost shows the highest R2 value of around 0.99, which indicates its higher prediction accuracy compared to other ML algorithms used. The ML‐based prediction results obtained in this work can be very useful for controlling the VRFB temperature rise during operation and act as an indicator toward further development of an optimized thermal management system.

Effects of pulse rise time and pulse width on discharge mode transition of SDBD plasma under repetitive pulses

Abstract Affected by environmental states and power supply parameters, the discharge mode of surface dielectric barrier discharge (SDBD) plasma may gradually transfer from O3 mode to NO x mode, resulting in various gas-phase species for different applications. Despite the intensive study of attempts to control this discharge mode transition by changing discharge conditions and power excitations in recent years, the effects of the pulse rise time and the pulse width on the discharge mode transition have not been discussed. In the present study, a SDBD was excited by repetitive pulses with different pulse rise times or pulse widths, and the time-varying concentrations of key long-lived species (O3 and NO2) were quantified. The results demonstrated that it was possible to modulate the discharge mode by adjusting pulse rise time/pulse width. The quenching of O3 was observed to occur at a faster rate and the mode transition was noted to occur at an earlier point in time as the pulse rise time decreased from 225 ns to 125 ns and the pulse width increased from 0.5 μs to 4 μs. The employment of a zero-dimensional model for the analysis of plasma chemical kinetics revealed that the reduction in pulse rise time and the prolongation of pulse width resulted in an increase in the mean vibrational energy of N2(v) and a more rapid electrode temperature rise caused by plasma heating. The former enhanced the generation of NO, while the latter accelerated the thermal decomposition of O3, thereby promoting the speed of mode transition.

Simulation of the electromagnetic scattering characteristics in inductively coupled plasma array for aircraft stealth

Abstract In this paper, an electromagnetic model of an inductively coupled plasma (ICP) array is developed to investigate its electromagnetic transmission characteristics under varying discharge conditions. The reliability of the model is enhanced by basing plasma parameters on simulation results from a fluid dynamics model. Electromagnetic simulations analyze the reflection spectrum of the inductively coupled plasma array, revealing the modulation mechanism of the plasma array. Findings indicate that as excitation power increases, the attenuation peak of the ICP array shifts to higher frequencies, and the attenuation bandwidth widens. By employing different power levels for the two layers in the Inductively Coupled Plasma (ICP) process, the array can achieve effective stealth performance with lower power consumption. Adjusting plasma discharge parameters to match specific scenarios enables the proposed plasma array to achieve varied modulation effects, expanding its potential applications in aircraft stealth.

Influence of Hydrogen Additive on Electrophysical Parameters and Emission Spectra of Tetrafluoromethane Plasma

The influence of the addition of hydrogen on the electrophysical parameters and emission spectra of tetrafluoromethane under conditions of a direct current glow discharge has been studied. It has been established that gas temperature changes nonlinearly with increasing proportion of hydrogen in the plasma-forming mixture. The emission spectra of tetrafluoromethane plasma with hydrogen were obtained and analyzed. It is shown that plasma radiation is represented by atomic and molecular components, and the dependences of the line radiation intensities on the external conditions of the discharge are determined by the excitation of emitting states during direct electron impacts.

Evaluation of the highly sinuous bend sequences using an ecohydraulic model to ascertain the suitability of fish habitats for river ecological conservation

The spatial and temporal patterns of river meanders play a crucial role not only in shaping the hydrogeomorphic properties of channels and floodplains but also in ascertaining the suitability of fish habitats for river ecological conservation. This study evaluates hydrodynamic features and fish habitat quality in six highly sinuous bend sequences along the Black River in China’s Qinghai plateau. The ecohydraulic model system was used to simulate hydrodynamics, habitat suitability, and sediment transport to determine the habitat quality of the highly sinuous bend sequences. The results have successfully identified differences in the suitability of various bends for fish spawning. Bend 1 was found to be the most suitable among all bends, while bends 5 and 6 were deemed unsuitable specifically during higher flood flows. Cases 3 and 4 were discovered to be highly suitable for fish habitat under normal discharge scenarios. Conversely, cases 5 and 6 were the least suitable, with cases 5 and 6 exhibiting the worst habitat quality, with HSI values nearing zero under high discharge. The study concluded that regulated discharge conditions could support fish spawning in these sinuous bends, with lower flood flows promoting habitat suitability and higher flood flows fragmenting habitat quality. The findings highlight the importance of diverse hydrodynamic and geomorphic conditions in creating suitable fish habitats across varying flow conditions, offering insights into the conservation and management of mountain rivers to protect endangered fish species and aquatic organisms.

Experimental study on the thermal management performance of immersion cooling for 18650 lithium-ion battery module

Direct liquid cooling technology stabilizes the battery module at the ideal operating temperature by leveraging the coolant's high heat capacity and its heat dissipation ability through circulation. This study introduced a forced-flow immersion cooling method employing transformer oil as the cooling medium for 18650 lithium-ion battery modules. The performance of this cooling system design was assessed under various discharge rates, coolant flow rates, and channel positions. Experimental data reveals that the average temperature of the oil-immersion-cooled battery module was around 26.3 % lower than that of the naturally air-cooled battery module under a 2 C discharge condition with zero flow rate. The cooling capacity of the oil-immersion system was limited, with theoretical analyses indicating an optimal flow rate of 200 mL/min. Four unique cooling channel configurations was designed: side center inlet to center outlet, side upper inlet to lower outlet, front upper inlet to lower outlet, and front center inlet to center outlet. The peak temperatures of the battery module for these setups were 31.7 °C, 31.0 °C, 31.0 °C, 31.0 °C, and 31.6 °C, respectively, with a temperature difference of about 2 °C. Notably, the side upper inlet to lower outlet channel configuration displays superior cooling performance. These findings contribute to advancing more efficient cooling systems and offer valuable insights for optimizing direct liquid-cooled thermal management systems for lithium-ion batteries in the future.

Study on the parameter identification of lithium-ion battery model under high-rate pulse discharge conditions

With the rapid development of the new energy sector and lithium-ion (Li-ion) battery technologies, using Li-ion batteries with high energy density and high discharge rate to form the primary energy subsystem can further improve the compactness and miniaturization level of pulsed power systems. In traditional electric power systems, Li-ion batteries normally operate under the discharge condition of low current and long time. However, as the main power source of pulsed power systems, it is required that Li-ion batteries should have the capability for high-rate pulse discharge. To ensure the safe and reliable operation of the battery energy storage system, it is necessary to conduct the parameter identification of the Li-ion battery model to establish an accurate pulse discharge model under such working conditions. This paper focuses on the discharge characteristics of a cylindrical high-rate single Li-ion battery with a capacity of 3 Ah. At a 20 C rate, the discharge data were obtained when the pulse repetition frequency was 10 Hz and the duty cycle was 90%. By using the mathematical expressions of the Thevenin equivalent circuit model, the recursive relationship of the circuit parameters was obtained, and the parameter identification of the battery model was conducted based on the experimental data by using the genetic algorithm. The results show that the fitting accuracy of the parameter identification reaches 0.9995, indicating the high precision of the model for the Li-ion battery. This work provides a significant reference for the modeling and parameter identification of Li-ion batteries under high-rate pulse discharge conditions.

Design and test of a module of a breathable Electrostatic Shield for the MITICA 1 MV negative ion Beam Source

The electrical insulation of the MITICA Beam Source at 1 MV is a challenging issue, which has not been fully addressed so far on the basis of experimental results and of theoretical models available in literature. Being MITICA the full-size prototype of the Heating Neutral Beam Injector for the ITER fusion experiment, its electrical insulation is constituted just by vacuum gaps and alumina insulators, since other insulating materials such as SF6 gas or fibreglass-reinforced plastic (FRP) would be quickly degraded by the expected neutron flux produced by fusion reaction. Extrapolations based on HV tests on reduced-scale models have recently indicated the risk of electrical breakdowns in the vacuum gap between electrodes nominally operating at -1 MV and the vacuum vessel (at ground potential). The risk of electrical breakdown can be mitigated by introducing an intermediate Electrostatic Shield (ES), which essentially is an equipotential (metallic) enclosure surrounding the HV electrode, so as to divide the vacuum gap in two independent insulating gaps of 400 kV and 600 kV respectively. However, for optimal negative ion production, the ion source shall operate in H2 or D2 at a pressure of ∼ 0.3 Pa and unavoidably produces a flow of gas leaking out in the surrounding vacuum. Thus, the presence of an intermediate shield can substantially increase the background gas pressure in the vacuum gaps, and, due to the large gap length (0.6 m), exacerbate the risk of breakdown when the pressure approaches the conditions of Paschen-type discharges. In addition to this, RF-induced breakdowns were found on the rear side of the ion source during the operation of the prototype source SPIDER, which were somewhat correlated to a relatively high hydrogen pressure in that area. For these reasons, a structure capable of constituting a full equipotential barrier all around the BS and, at the same time, having sufficient gas conductivity (breathability) to allow efficient pumping of background gas, has been designed. In the first part of the paper, the requirements and design optimization of a breathable module of the intermediate ES are described. Then, an experimental campaign for the validation of the electrode implementation the test configurations and the experimental procedure is discussed.

Mutitask Learning Network for Partial Discharge Condition Assessment in Gas-Insulated Switchgear

Mutitask Learning Network for Partial Discharge Condition Assessment in Gas-Insulated Switchgear

Physics-informed neural networks for inversion of river flow and geometry with shallow water model

The river flow transports sediment, resulting in the formation of alternating sandbars in the riverbed. The underlying physics is characterized by the interaction between flow and river geometry, necessitating an understanding of their inseparable relationship. However, the dynamics of river flow with alternating sandbars are hard to understand due to the difficulty of measuring flow depth and riverbed geometry during floods with current technology. This study implements an innovative approach utilizing physics-informed neural networks (PINNs) to estimate important hydraulic variables in rivers that are difficult to measure directly. The method uses sparse yet obtainable flow velocity and water level data. The governing equations of motion, continuity, and the constant discharge condition based on the mass conservation principle are integrated into the neural network as physical constraints. This approach enables the completion of sparse velocity fields and the inversion of flow depth, riverbed elevation, and roughness coefficients without requiring direct training data for these variables. Validation was performed using model experiment data and numerical simulations derived from these experiments. Results indicate that the accuracy of the estimations is relatively robust to the number of training data points, provided their spatial resolution is finer than the wavelength of the sandbars. The inclusion of mass conservation as a redundant constraint significantly improved the convergence and accuracy of the model. This PINNs-based approach, using measurable data, offers a new way to quantify complex river flows on alternating sandbars without significant updates to conventional methods, providing new insights into river physics.

VUV imaging of type-I ELM filamentary structures and their temporal characteristics on EAST

In the H-mode experiments conducted on the Experimental Advanced Superconducting Tokamak (EAST), fluctuations induced by the so-called edge localized modes (ELMs) are captured by a high-speed vacuum ultraviolet (VUV) imaging system. Clear field line-aligned filamentary structures are analyzed in this work. Ion transport induced by ELM filaments in the scrape-off layer (SOL) under different discharge conditions is analyzed by comparing the VUV signals with the divertor probe signals. It is found that convective transport along open field lines towards the divertor target dominates the parallel ion particle transport mechanism during ELMs. The toroidal mode number of the filamentary structure derived from the VUV images increases with the electron density pedestal height. The analysis of the toroidal distribution characteristics during ELM bursts reveals toroidal asymmetry. The influence of resonance magnetic perturbation (RMP) on the ELM size is also analyzed using VUV imaging data. When the phase difference of the coil changes periodically, the widths of the filaments change as well. Additionally, the temporal evolution of the ELMs on the VUV signals provides rise time and decay time for each single ELM event, and the results indicate a negative correlation trend between these two times.

Battery temperature estimation at wide C-rates using the LSTM model based on polarization characteristics

Data-driven based approaches have achieved significant results in estimating battery temperature. Nevertheless, the present challenge emanates from the dearth of theoretical guidance governing the training strategy of the model, leading to an inefficient training process and constrained accuracy, especially under specific conditions. In this paper, a Long Short-Term Memory (LSTM) neural network based on polarization characteristics is proposed to estimate the battery discharge temperature. Firstly, the LSTM temperature estimation model is established and the hyperparameters are optimized by Genetic Algorithm (GA). The results show that the accuracy of the traditional model is less satisfactory, with the Maximum Error (ME) of 3.79 °C. Subsequently, the voltage and polarization heat production characteristics of the battery are analyzed under various discharge conditions. It is found that the heat production of the battery is smaller at 0–1.5C, and the heat production characteristics are similar at medium-rate, but the change of heat production is significant when C-rate exceeds 3C. Finally, a LSTM temperature estimation model based on polarization characteristics is proposed, which divides the datasets according to heat production characteristics. Further, a strategy for predicting temperatures in later stages based on limited data from the current condition is also proposed, this mothed combines with the transfer learning method to rapidly develop models for different high-rate conditions. The ME of the model on the test set is 1.1 °C, and the training time is reduced by 32.93 s. The results show that the LSTM temperature estimation model based on polarization characteristics has higher accuracy and training efficiency than the traditional LSTM temperature estimation model.

LANDSCAPE CHANGES BASED ON HYDROLOGICAL CHARACTERISTICS IN THE WAE RIUAPA WATERSHED WEST SERAM REGENCY, MALUKU, INDONESIA

Changes in the Wae Riuapa River Basin landscape in West Seram Regency, Maluku, reflect complex ecological and social dynamics. The Wae Riuapa watershed is an integral part of the hydrological cycle in the region, influencing water availability, water quality, and geomorphological processes. This research aims to model changes in the landscape of the Wae Riuappa watershed regarding hydrological characteristics. The research method uses ArcGIS 10.3 for spatial analysis of 2010 and 2020 Landsat images and hydrooffice 12.0 software, BFLOW 3.0 module, a hydrological model for graphical separation of daily discharge. The results of modeling landscape changes in the Wae Riuappa watershed on hydrological characteristics obtained a regression model that depicts landscape changes as having a significant simultaneous and partial influence on discharge conditions in the Wae Riuappa watershed during the research period. The spatial pattern of area distribution for the nine land cover types in the study period was relatively similar. Widespread forest land cover still dominates the landscape of the Wae Riuapa watershed. However, the pattern of widespread distribution of changes is starting to decrease in primary and secondary forest land cover. Meanwhile, the distribution pattern of changes in agricultural land and mixed gardens and settlements has increased slowly and significantly. The results of a multi-temporal analysis of the hydrological characteristics of the Wae Riuapa watershed show that the daily discharge trend experiences multi-temporal fluctuations.