System Parameters Research Articles

Effects of the internal temperature on vertical mixing and on cloud structures in ultra-hot Jupiters

The vertical mixing in hot-Jupiter atmospheres plays a critical role in the formation and spacial distribution of cloud particles in their atmospheres. This affects the observed spectra of a planet through cloud opacity, which can be influenced by the degree of cold trapping of refractory species in the deep atmosphere. We aim to isolate the effects of the internal temperature on the mixing efficiency in the atmospheres of ultra-hot Jupiters (UHJs) and the spacial distribution of cloud particles across the planet. We combined a simplified tracer-based cloud model, a picket fence radiative-transfer scheme, and a mixing length theory to the Exo-FMS general circulation model. We ran the model for five different internal temperatures at typical UHJ atmosphere system parameters. Our results show the convective eddy diffusion coefficient remains low throughout the vast majority of the atmosphere, with mixing dominated by advective flows. However, some regions can show convective mixing in the upper atmosphere for colder interior temperatures. The vertical extent of the clouds is reduced as the internal temperature is increased. Additionally, a global cloud layer gets formed below the radiative--convective boundary (RCB) in the cooler cases. Convection is generally strongly inhibited in UHJ atmospheres above the RCB due to their strong irradiation. Convective mixing plays a minor role compared to advective mixing in keeping cloud particles aloft in UHJs with warm interiors. Higher vertical turbulent heat fluxes and the advection of potential temperature inhibit convection in warmer interiors. Our results suggest that isolated upper atmosphere regions above cold interiors may exhibit strong convective mixing in isolated regions around Rossby gyres, allowing aerosols to be better retained in these areas.

Dynamic Analysis of Geared Rotor System with Hybrid Uncertainties

Current research on the dynamics and vibrations of geared rotor systems primarily focuses on deterministic models. However, uncertainties inevitably exist in the gear system, which cause uncertainties in system parameters and subsequently influence the accurate evaluation of system dynamic behavior. In this study, a dynamic model of a geared rotor system with mixed parameters and model uncertainties is proposed. Initially, the dynamic model of the geared rotor-bearing system with deterministic parameters is established using a finite element method. Subsequently, a nonparametric method is introduced to model the hybrid uncertainties in the dynamic model. Deviation coefficients and dispersion parameters are used to reflect the levels of parameter and model uncertainty. For example, the study evaluates the effects of uncertain bearing and mesh stiffness on the vibration responses of a geared rotor system. The results demonstrate that the influence of uncertainty varies among different model types. Model uncertainties have a more significant than parametric uncertainties, whereas hybrid uncertainties increase the nonlinearities and complexities of the system’s dynamic responses. These findings provide valuable insights into understanding the dynamic behavior of geared system with hybrid uncertainties.

Analysis and Suppression of Oscillations in Doubly Fed Variable Speed Pumped Storage Hydropower Plants Considering the Water Conveyance System

The doubly fed variable speed pumped storage (DFVSPS) system is a hydraulically, mechanically, and electrically coupled system, and the characteristics of the components from the water conveyance system to the transmission line need to be fully considered in the oscillation analysis. Hence, the model of the water conveyance system is included to investigate the oscillation characteristics of the DFVSPS connecting to the grid via a series-compensated line. A small-signal state-space model of the DFVSPS system in the generation mode is first established. The oscillation characteristics of the DFVSPS are studied, and the dominant state variables for each oscillation mode are identified. The impact of system parameters on oscillations is further studied, and simulations are carried out to validate the accuracy of the model. The results indicate the oscillation mode of the DFVSPS comprises the electrical sub-synchronous oscillation (SSO) mode and the hydraulically, mechanically coupled low-frequency mechanical oscillation modes. When the series compensation level is high, the SSO becomes divergent, and the system is more likely to be unstable. Optimizing the rotor-side control parameters and the governor control parameters, sub-synchronous and low-frequency oscillations could be effectively suppressed, respectively. This study provides reference suggestions for the development and use of the future DFVSPS system.

Spatiotemporal fluctuation induces Turing pattern formation in the chemical Brusselator

Chemical reactions are embedded in spatiotemporal fluctuations instead of a constant environment. Here, we aimed to assess reaction–diffusion (RD) with dichotomous noise‐controlling system parameters in the Brusselator and examine the effect of these fluctuations on the dynamic behavior of chemical reactions. By performing a multiscale perturbation analysis, we demonstrated that the correlated noise can broaden the Turing region even if molecular memory (autocorrelation time) exists. However, for small noise, short‐term memory promotes Turing instability. The instability of the Brusselator is determined by the noise strength, which belongs to the optimal region if the diffusion coefficient is fixed. Turing pattern selection and stability are also governed by the dynamic character of the amplitude equation, and the entire Turing instability region shifts to the right in the phase space with noise perturbation. Finally, numerical simulations validate the theoretical derivation that correlated noise can amplify Turing pattern formation to maintain distinct patterns.

Measurement of Oscillatory and Dissipative Resonator Characteristics of Solid-State Wave Gyroscopes: Algorithms for Operational Correction of Systematic Signal Drift

The article discusses the possibilities of increasing the output signal accuracy of integrating solid-state wave gyroscopes manufactured with a reduction in the requirements for the residual characteristics of the multi-frequency and multi-factor ratios of their resonators, as well as for the centering of the control ring electrode. These capabilities involve the use of a more complex output function that includes an additional part due to the effect of these errors on the systematic drift function. However, its formation requires introduction of an interval shutdown of the parametric excitation system so as to increase the purity of identification of the listed factors. To reveal this topic, the following is sequentially described: mathematical formulation of the problem; general analysis of the systematic drift of the integrating gyroscope signal caused by mechanical errors in the design of its resonator; autonomous algorithmic reduction of the influence of the dominant residual differentials in the mode of interval disconnection of excitation; autonomous algorithmic reduction of the influence of the dominant residual frequency difference in the mode of interval disconnection of excitation; simultaneous algorithmic compensation of the influence of residual Q and Frequency in the interval disconnection mode of excitation; estimation of the signal drift component of the integrating gyroscope, due to errors in the control loops, as well as the possibility of its reduction. The analysis of these problems is carried out on the basis of a model of wave processes in the gyroscope resonator, obtained only on the basis of the laws of classical mechanics. There were no additional errors due to the imperfection of the electrical circuits for processing measurement and control signals. Various computational schemes discussed in the article can also be useful for performing operational adjustments of the gyroscope measurement system, which may be required as a result of the design aging factor, as well as after its intensive use in a wide range of external disturbances.

Oral State and Salivary Cortisol in Multiple Sclerosis Patients

Background: MS patients experience gradual and progressive functional limitation, bulbar symptoms, cognitive dysfunction, and psychiatric disorders that can impinge on oral status. This study aimed to investigate the oral state, oral hygiene habits, and salivary cortisol levels in patients with relapsing-remitting multiple sclerosis (RRMS) compared to healthy controls. It also evaluated systemic parameters: disease duration, type of Disease Modifying Therapy (DMT), disability score, professional activity, and smoking in the study group. Methods: This study included 101 patients (71 women and 30 men, aged 16–71 years) and 51 healthy volunteers (36 women and 15 men, aged 28–82 years). The oral examination assessed the number of teeth, type and number of dental fillings and prosthetic restoration, oral hygiene state, and salivary cortisol. Results: It was found that MS patients had significantly more professional activity, swallowing problems, pronunciation issues, dry mouth, and taste disturbances than the control group. They brushed their teeth twice daily significantly less often. The API was higher, while the SBI was lower in MS patients. Disease duration positively correlated with age and number of missing teeth. The Expanded Disability Status Scale positively correlated with age, disease duration, number of missing teeth, number of composite fillings, and right and left-hand Nine Hole Peg test scores, and negatively correlated with the Sulcus Bleeding Index. Salivary cortisol levels did not differ between groups and correlated only with the disability scale. Conclusions: MS patients require ongoing dental care and preventive measures to manage both general and oral health symptoms effectively.

Enhancing Energy Harvesting Efficiency from Ambient Sources through Advanced Materials and Nanotechnology

The utilization of ambient energy sources for powering small-scale electronic devices and sensors has garnered significant attention due to its potential for sustainable and autonomous operation. This research focuses on enhancing the efficiency of energy harvesting from ambient sources, such as vibrations, heat differentials, and electromagnetic fields, through the integration of advanced materials and nanotechnology into energy harvesting systems. The study begins with a comprehensive review of existing energy harvesting technologies, highlighting their limitations in terms of efficiency, scalability, and adaptability to various ambient energy sources. It identifies the key challenges faced in achieving high energy conversion rates and explores the potential of advanced materials and nanoscale structures to address these challenges. One aspect of the research involves the development and characterization of novel materials with superior energy conversion properties. This includes the synthesis of piezoelectric materials for vibration energy harvesting, thermoelectric materials for heat-to-electricity conversion, and nanomaterials for enhancing electromagnetic energy harvesting efficiency. Advanced characterization techniques, such as scanning electron microscopy (SEM) and X-ray diffraction (XRD), are employed to analyze the structural and electrical properties of these materials. Furthermore, the study investigates the design and fabrication of nanostructured energy harvesting devices optimized for specific ambient energy sources. This involves the integration of nanoscale components, such as nanostructured electrodes, into energy harvesting systems to improve energy capture and conversion rates. Finite element analysis (FEA) simulations and experimental testing are conducted to evaluate the performance and efficiency of these nano-enhanced energy harvesting devices under real-world conditions. The research also addresses the optimization of energy harvesting system parameters, including resonance tuning, impedance matching, and energy management circuits, to maximize energy extraction and utilization. Computational modeling techniques, such as multiscale modeling and finite element simulations, are utilized to optimize system configurations and improve overall energy harvesting efficiency. Overall, this research contributes to the advancement of energy harvesting technologies by leveraging advanced materials and nanotechnology, paving the way for sustainable and autonomous power generation from ambient energy sources for a wide range of applications.

Strong-Coupling Modification of Singlet-Fission Dynamical Pathways.

We investigate theoretically the influence of strong light-matter coupling on the initial steps of the phototriggered singlet-fission process. In particular we focus on intramolecular singlet fission in a TIPS-pentacene dimer derivative described by a vibronic Hamiltonian including the optically active singlet excited states, doubly excited and charge transfer states, as well as the final triplet-triplet pair state. Quantum dynamics simulations of up to four dimers in the cavity indicate that the modified resonance condition imposed by the cavity strongly quenches the passage through the intermediate charge transfer and double-excitation states, thus largely reducing the triplet-triplet yield in the bare system. Subsequently, we modify the system parameters and construct a model Hamiltonian where the optically active singlet excitation lies below the final triplet-triplet state such that the yield of the bare system becomes insignificant. In this case we find that using the upper polariton as the doorway state for photoexcitation can lead to a much enhanced yield. This pathway is operative provided that the system is sufficiently rigid to prevent vibronic losses from the upper polariton to the dark-states manifold.

Optimization of power system parameters for multi-mode hybrid electric vehicles based on regional demand

The performance of the vehicle power system for multi-mode hybrid electric vehicle (M-MHEV) can be improved through meticulous parameter matching and optimization. This paper developed the powertrain parameter design and dynamic performance calculation program based on the structure design, parameter matching calculation, and powertrain system selection. A HOQM of M-MHEV is formulated to ascertain the weight coefficient of vehicle performance indicators according to different regional requirements and a parameter matching and optimization method for power system, employing the Particle Swarm Optimization (PSO) algorithm, is suggested to assess and harmony the vehicle’s performance. Firstly, a house of quality model of M-MHEV is constructed to ascertain the weight coefficient of vehicle performance indicators derived from different regional demands. Additionally, a method is introduced for optimization of power system parameters of M-MHEV based on regional demand, the PSO algorithm is employed to optimize the characteristic parameters. And sensitivity analysis of characteristic parameters is conducted relying on user needs in different regions. The simulation results show that users in the northern and southern regions have different final weight coefficients for vehicle performance indicators and the parameters not only exert a substantial individual influence but also exhibit an interaction effect on the vehicle performance. Finally, a series of comparative simulations, are carried out with two optimization parameters respectively, the simulation outcomes demonstrate that the solution obtained through optimized design parameters solution could markedly enhance the technical parameters of the vehicle. The efficacy and viability of the proposed method based on user needs in different regions are verified. The conclusion provides a useful reference for differentiated design.

Artificial intelligent-based backstepping control of parallel-connected multi-machine wind generation system using a new fifteen-switch rectifier

This academic work offers a new variable-speed wind power generation system (NVS-WPGS) that employs two 5-phase permanent magnet synchronous generators (PMSGs) controlled through a fifteen-switch rectifier (FSR) architecture for a system that’s integrated to the power grid. The two 5-phase PMSGs are linked to the DC bus through an FSR, which, in turn, connects the DC link to the grid via a three-phase inverter. To ameliorate the operational efficiency of the wind system under investigation, the backstepping control (BSC) approach was employed. Building upon this technique, the Adaptive Neuro-Fuzzy Inference System (ANFIS) was trained and implemented for controlling the wind system. The goal is to boost system performance and control. While BSC is a strong control method for reference signal tracking, its dependence on system parameters is a downside. This dependence typically degrades performance. On the other hand, ANFIS exhibits robustness to variations in system parameters and uncertainties. The efficaciousness of the suggested control methodology was evaluated via experimental investigations conducted using the Matlab/Simulink program. The simulations were performed to verify the system’s capacity to maintain controlled variables in accordance with desired references, even in the presence of fluctuations in wind velocity. Simulations exhibit that the suggested ANFIS-BSC control system accurately tracks controlled intended references. Furthermore, system performance improved. Speed overshoot was decreased by 100%. Additionally, the system’s efficiency was enhanced, reaching a level of 96.5%, beating the BSC approach’s 96%. These findings underscore the remarkable effectiveness of the ANFIS-BSC approach compared to BSC methodologies.

Model-predictive optimal control of ferrofluidic microrobots in three-dimensional space

Ferrofluid microrobots have emerged as promising tools for minimally invasive medical procedures. Their unique properties to navigate complex fluids and reach otherwise inaccessible regions of the human body have enabled new applications in targeted drug delivery, tissue engineering, and diagnostics. This paper proposes a model-predictive controller for the external magnetic manipulation of ferrofluid microrobots in three dimensions (3D). The internal optimization routine of the controller determines appropriate changes in the applied electromagnetic field to minimize the deviation between the actual and desired trajectories of the microrobot. A linear system governing locomotion is derived and used as the equality constraints of the optimization problems associated with the feedback index. In addition to ferrofluid droplets, the controller presented in this work may be applied to other magnetically-pulled microrobots. Several experiments are performed to validate the controller and showcase its ability to adapt to changes in system parameters such as the desired tracking trajectory and the size, orientation, deformation, and velocity of the microrobot. The accuracy of the controller is analyzed for each experiment, and the average error is found to be within 0.25 mm for small velocities. An additional experiment is performed to demonstrate significant improvement over a PID controller that is optimally tuned using Bayesian optimization. The results presented in this paper suggest that the proposed control algorithm could enable new microrobotic capabilities in minimally invasive medical procedures, lab-on-a-chip applications, and microfluidics.

CFD Analysis to Optimize Air Quality in Aeroponic Tower Garden

Aeroponic tower gardens represent an innovative approach to urban agriculture, utilizing vertical structures to cultivate plants without soil. Research highlights the efficiency of this method in optimizing resource usage, such as water and space, compared to traditional farming. Studies emphasize increased crop yields, faster growth rates, and reduced environmental impact. Additionally, aeroponic systems enable precise nutrient delivery to plants, fostering optimal growth conditions. While the technology shows promise, further investigations are needed to address potential challenges and optimize its application in diverse agricultural settings. The study presents the simulation of air temperature and humidity distribution in a closed chamber of the vertical farm for optimization purposes. The environmental conditions inside the chamber are optimized for plant growth, to maintain the temperature at a range of 18-24 °C temperature and the relative humidity at a range of 75-85 %. The paper also discusses the challenges, limitations, and future recommendations for improving the system. Simulation of the system, on the other hand, is crucial not only for prototype fabrication but also for the optimization of sensor locations. The simulation of the tower garden system was performed using ANSYS-FLUENT to portray how the airflow inside the tower garden's enclosed environment was distributed. The system parameters such as humidity and temperature were set to meet the plants' growth requirements, and the system was treated as the steady state with an adiabatic boundary. Based on the simulation results, the best design recommended is to use two humidity and temperature sensors, one at the top and the other at the core of the prototype. Hence, a complete mapping of temperature and humidity distribution that is critical for plant growth would be occurred.

Machine learning stochastic differential equations for the evolution of order parameters of classical many-body systems in and out of equilibrium

Abstract We develop a machine learning algorithm to infer the emergent stochastic equation governing the evolution of an order parameter of a many-body system. We train our neural network to independently learn the directed force acting on the order parameter as well as an effective diffusive noise. We illustrate our approach using the classical Ising model endowed with Glauber dynamics, and the contact process as test cases. For both models, which represent paradigmatic equilibrium and nonequilibrium scenarios, the directed force and noise can be efficiently inferred. The directed force term of the Ising model allows us to reconstruct an effective potential for the order parameter which develops the characteristic double-well shape below the critical temperature. Despite its genuine nonequilibrium nature, such an effective potential can also be obtained for the contact process and its shape signals a phase transition into an absorbing state. Also, in contrast to the equilibrium Ising model, the presence of an absorbing state renders the noise term dependent on the value of the order parameter itself.

Soil Water Characteristics Curves (SWCC) of Residual Soils Stabilized using Emerging and Novel Bioinspired Technique

The development of unsaturated soil mechanics stemmed from the inadequacies of classical soil mechanics in addressing the partial pore spaces found in real-world soil systems. However, assessing soil parameters under partial saturation is resource-intensive. To streamline this, the Soil-Water Characteristic Curve (SWCC) theory was introduced. Widely applied across geotechnical engineering, water resources, and agriculture, SWCC predicts parameters of unsaturated soil systems. In geotechnical engineering, SWCC is vital for designing systems near the Earth's surface, like slopes, clay liners and foundations. Furthermore, most of conventional soil improvement techniques pose a lot of environmental concerns. thus, emergence of green construction materials widely known as bio mediated and bioinspired soil improvement techniques like Enzymatic induced calcite precipitation (EICP). EICP, an emerging eco-friendly as it employs use of simply chemical compound that reduces carbon footprints, bioinspired technique as it mimics natural forming process, was applied to stabilize tropical soil for SWCC determination. SWCC parameters were derived by subjecting EICP-treated soils at varying concentration of cementation solutions (0 – 1.00 M) using pressure plate method. Results revealed that saturated water content θs, retention water content θr, and air entry value Ψa increased with higher cementation solution concentration. The values of θs, θr, and Ψa improved from of 0.663, 0.23405, and 3.4832 at 0 concentration of cementation solution to 0.701, 0.22864, and 7.7086, respectively upon treatment with 1.00 M cementation solution. Finally, it has been demonstrated EICP is capable of improving the SWCC parameters of residual soil, thus the EICP technique can be exploited in stabilization of residual for construction of compacted clay liner.

Robustness estimation for state-of-charge of a lithium-ion battery based on feature fusion

State of charge (SOC) of the lithium-ion battery stands as one of the crucial parameters of the battery management system. To enhance the robustness and accuracy of data-driven methods for estimating SOC, in this study, a novel framework based on feature selection and closed-loop estimation framework is proposed for estimating the SOC of a battery. Firstly, the maximum mutual information coefficient is used to correlate the data collected from the battery experiments. Secondly, an improved least squares support vector machine model (LSSVM) is proposed for SOC estimation using the sine cosine optimization algorithm for parameter optimization of LSSVM. Furthermore, based on the Sage-Husa adaptive Kalman filter, this study constructed a closed-loop estimation model. Finally, we conduct experiments on batteries with variable temperatures and working conditions, to verify its robustness and generalization. The results indicate that the proposed method has great SOC estimation accuracy compared to the control group. Even under simulated measurement noise conditions, the proposed method achieves the root mean square error 1.28 % and 1.12 % respectively, demonstrating strong robustness.

Modeling the Dynamic Response of a Light-Driven Liquid Crystal Elastomer Fiber/Baffle/Spring-Coupled System

Abstract A self-oscillating system can harness energy from the external environment to sustain its continuous motion, making it highly suitable for applications in soft robotics, military industry, energy, and other fields. This article introduces a system composed of a light-responsive liquid crystal elastomer fiber, a baffle, and a spring, capable of achieving self-oscillation under stable lighting conditions. Considering the established dynamic model of light-responsive liquid crystal elastomers (LCEs), a nonlinear dynamic model is introduced and used as the basis for exploring its dynamic characteristics. Numerical computations reveal that the coupled system exhibits two distinct motion states: self-oscillation and static states. The behavior of the system is sustained through the interaction of light energy and damping dissipation. Furthermore, a detailed investigation is conducted on the key system parameters affecting the frequency and amplitude of self-oscillation. In contrast to the complexity of current self-oscillating systems, this particular self-oscillating system features simplicity in structure, ease of manufacture, and strong adaptability. These advantages are expected to provide broader design possibilities for micro-machines and mechanical production processes.

Research on Cascaded On‐Board DC/DC Converter Based on Three‐Phase Interleaved LLC

ABSTRACTThe on‐board DC/DC converter proposed in this research consists of a Buck converter and a three‐phase interleaved LLC resonant converter. This paper analyzes the average current and output current ripple characteristics of the rear‐stage converter in addition to explaining the topology and working principles of the front‐stage Buck converter and the rear‐stage three‐phase interleaved LLC resonant converter. To establish a single‐phase fundamental equivalent circuit for the rear‐stage converter, apply the fundamental wave analysis method, and it is essential for comprehending the impact of relevant system parameters on voltage gain characteristics and resonant operating regions. Conducting research on the two‐stage on‐board DC/DC converter's control strategy involves establishing small‐signal circuit models for both front and rear stage converters, derivation of parameters for double closed‐loop control, and determination of the control strategy employing phase‐shifted current sharing for the rear‐stage converter in cases where significant tolerance exists in resonant components. The simulation for a two‐stage on‐board DC/DC converter results indicates that under various conditions of resonant component tolerance, the converter is capable of achieving balanced phase currents and demonstrates minimal output current ripple prior to capacitor filtering. A 1.5‐kW experimental prototype has been fabricated, and the experimental findings indicate that the output voltage remains stable at 13.8 V despite fluctuations in input voltage. The highest efficiency of the prototype is about 96.27%, and the utilization of phase‐shifting current balancing control results in a notable reduction in output current ripple. This provides more evidence that the design of a two‐stage on‐board DC/DC converter is correct.

Valence state and redox property of Fe‐CNTs added CeO2/TiO2 catalyst

AbstractThe investigation focused on examining the impact of incorporating Fe‐carbon nanotubes (CNTs) on the valence state and oxygen vacancies, and the physicochemical parameters of the catalytic system. For the CeO2/TiO2 catalyst with Fe‐CNTs, no distinct X‐ray diffraction (XRD) peaks corresponding to Fe‐CNTs were observed. This indicated their uniform dispersion, supported by transmission electron microscopy (TEM) and scanning electron microscopy (SEM) imaging. X‐ray photoelectron spectroscopy (XPS) analysis was conducted to observe the changes in the valence state due to the addition of Fe‐CNTs. The concentration of Ce3+ on catalyst surface increased from 19.90% to 29.48% with an increased concentration of the chemisorbed oxygen species (Oα). This suggests that the addition of Fe‐CNTs into CeO2/TiO2 catalyst reduced Ce4+ on the catalyst surface, resulting in the formation of oxygen vacancies. Oxygen temperature‐programmed desorption (O2‐TPD) and hydrogen temperature‐programmed reduction (H2‐TPR) analyses showed both the adsorbed oxygen species and H2 consumption increased with adding Fe‐CNTs. Furthermore, the onset temperatures for O2 desorption and H2 consumption became lower, confirming the enhancement of catalytic redox properties.

Structure and dynamic viscosity of CaO–SiO2 and CaO–SiO2–B2O3 model slag systems

Correlation dependencies between the dynamic viscosity of slag and its structural parameters were studied to determine an optimal basicity of silicon smelting slag under the addition of boron oxide to eliminate slagging of the bottom of ore-smelting furnaces. Experimental studies were conducted on CaO–SiO2 and CaO– SiO2–B2O3 model slags obtained at 1600°С. Raman spectroscopic analysis was carried out using a Horiba JobinYvon HR800UV analyzer (France). Theoretical calculations of slag viscosity were performed using Urbain and Mills models. During the experiments, the key structural parameters of slag systems varied within the following limits: the experimental Raman spectrum deconvolution function from 1.41 to 2.45 and optical basicity from 0.58 to 0.68. The obtained experimental and theoretical data were related by mathematical dependencies. It was found that the dynamic viscosity of slag can be promptly determined by Raman spectroscopy on the basis of mathematical models. The dependence obtained shows that slag viscosity decreases upon an increase in the number of bridging oxygen atoms in the silicate anion structure. Notably, this decrease in slag viscosity is observed up to the value of the experimental Raman spectrum deconvolution function of ~1.55-1.60 or slag optical basicity of 0.60–0.62. When B2O3 is added, the viscosity undergoes a further decrease. In practice, for CaO–SiO2 slag systems, the use of boroncontaining flux as a liquefying agent is reasonable at CaO/SiO2 = 0.61–0.63 while maintaining the content of B2O3 in the slag at a level of 1%. The two models (classical and modified) proposed by Urbain were established to be more suitable for theoretical calculation of viscosity in CaO–SiO2 and CaO–SiO2–B2O3 systems. Mills’ model is not suitable for these purposes, since the correlation coefficients in the corresponding mathematical model are not sufficiently large. Further research in this direction is required in order to establish appropriate dependencies of slag viscosity on its structural parameters at different temperatures.

A Novel Hybrid Harris Hawk Optimization–Sine Cosine Algorithm for Congestion Control in Power Transmission Network

In a deregulated power system, managing congestion is crucial for effective operation and control. The goal of congestion management is to alleviate transmission line congestion while adhering to system constraints at minimal cost. This research proposes a hybrid Harris Hawk Optimization–Sine Cosine Algorithm (hHHO-SCA) for an efficient generation rescheduling approach to achieve the lowest possible congestion cost. The hybridization has been performed by introducing the features of SCA in the HHO to boost the exploration and exploitation steps of HHO, providing an efficient global solution and effectively optimizing rescheduled power output. The effectiveness of this methodology is evaluated using IEEE 30 and IEEE 118-bus test systems, taking into account system parameters. The potency of the proposed method is analyzed by comparing the results of the hHHO-SCA with those from other recent optimization techniques. The findings show that the hHHO-SCA outperforms other methods by avoiding local optima and demonstrating promising convergence characteristics.