System Model Research Articles

Dynamics analysis of rotor in disc centrifuge for separation of bioengineering

In order to reveal the influence of gyroscope effect and structure parameters on the modal frequency and critical speed of the rotor system in disc centrifuge for separation of bioengineering. Based on the rotor rigid body dynamics, the fixed-point gyroscope motion of the rotating spindle of the disc centrifuge was analyzed. Establishing a finite element dynamic model for the rotor system of a disc centrifuge, taking into account the gyroscopic moment caused by the rotational inertia of the rotor disk and the elastic support of the bearings. We calculated the gyroscopic moment and the analytical expression of critical speed. Analyzing the quantitative relationship between the gyroscope effect, bearing support stiffness, drum material density and the critical speed of the rotor system. The results show that the calculated value of finite element is close to the measured value. The influence of gyroscopic force on the vibration characteristics of rotor system for dish centrifuge cannot be ignored. The critical speeds of the rotor system increase with the increase of elastic support stiffness, while the first critical speed decreases with the increase of drum density, but the second critical speed increases with the increase of drum density. These studies provide a theoretical reference for the dynamic response, structural design and dynamic balance of disc centrifuge.

A Comprehensive Review of the Pseudo‐Two‐Dimensional (P2D) Model: Model Development, Solutions Methods, and Applications

AbstractRecently, a paradigm shift toward the tremendous use of portable and mobile consumer products is observed and it consequently increases the battery demand. Currently, lithium‐ion batteries (LIBs) are used in almost all kinds of devices, however, the increased usage of LIBs raises some safety concerns. For safe operation, it is necessary to investigate the all important chemical and physical features of LIBs. Several mathematical models are proposed such as equivalent circuit models (ECMs), physics‐based ECM and electrochemical models (EMs). Among them pseudo‐two‐dimensional (P2D) model is most famous and comprehensive physics‐based EM and used frequently to study the battery electrochemical phenomena. Hence, the focus of this review work is to summarize the reported literature on the P2D model governing laws, its simplified models, coupled models, solution techniques, parameter analysis, and its applications. The literature survey shows that the P2D and its simplified models are very helpful to analyze the important aspects of LIBs such as state of charge, state of health, recycling, battery manufacturing and design etc. Also, it can be seen that no single model can be fit to study every aspect of LIBs and design a comprehensive battery management system (BMS) but different models can be used to address the specific applications.

Exploring the Impact of Nature-Based Solutions for Hydrological Extremes Mitigation in Small Mixed Urban-Forest Catchment

Many regions in Europe face increasing issues with flooding and droughts due to changing rainfall patterns caused by climate change. For example, higher rainfall intensities increase urban flooding. Nature-based solutions (NbS) are suggested as a key mitigation strategy for floods. This study aims to address and mitigate the challenges faced in Tivoli natural park in Ljubljana regarding high peak discharges and low-flow issues in the creek entering the sewer system. The study involves setting up, calibrating and validating a Hydrologic Engineering Centre–Hydrologic Modelling System (HEC-HMS) model using available data. This study analyses NbS, such as small ponds, green roofs and permeable paving, to reduce peak discharge. Runoff was reduced by an average of 32.4% with all NbS implemented and peak discharge by 20 L/s. Permeable parking performed best, with an average runoff reduction of 6.4%, compared to 4.8% for permeable streets and 5.9% for green roofs. The ponds reduced peak discharge, although their effectiveness varied between rainfall events. Rainfall events with higher volumes and durations tended to overwhelm the proposed solutions, reducing their effectiveness. The ability of HEC-HMS to model NbS is also discussed. The curve number (CN) parameter and impervious % alterations to simulate NbS provided quantitative data on changes in runoff and discharge.

CHUWD-H v1.0: a comprehensive historical hourly weather database for U.S. urban energy system modeling

Reliable and continuous meteorological data are crucial for modeling the responses of energy systems and their components to weather and climate conditions, particularly in densely populated urban areas. However, existing long-term datasets often suffer from spatial and temporal gaps and inconsistencies, posing great challenges for detailed urban energy system modeling and cross-city comparison under realistic weather conditions. Here we introduce the Historical Comprehensive Hourly Urban Weather Database (CHUWD-H) v1.0, a 23-year (1998–2020) gap-free and quality-controlled hourly weather dataset covering 550 weather station locations across all urban areas in the contiguous United States. CHUWD-H v1.0 synthesizes hourly weather observations from stations with outputs from a physics-based solar radiation model and a reanalysis dataset through a multi-step gap filling approach. A 10-fold Monte Carlo cross-validation suggests that the accuracy of this gap filling approach surpasses that of conventional gap filling methods. Designed primarily for urban energy system modeling, CHUWD-H v1.0 should also support historical urban meteorological and climate studies, including the validation and evaluation of urban climate modeling.

Modelling the transformation of energy-intensive industries based on site-specific investment decisions

The transition towards climate-neutral industry is a challenge, particularly for heavy industries like steel and basic chemicals. Existing models for assessing industrial transformation often lack spatial resolution and fail to capture individual investment decisions. Consequently, the spatial interplay between industry transformation, energy availability, infrastructure availability, and the dynamics of discrete investments is inadequately addressed. Here we present a site-specific approach that considers individual industrial sites to simulate discrete investment decisions. The investment decision is modelled as a discrete choice among alternative technologies with their total cost of ownership as the main decision criterion. Process costs depend on the scenario-specific assumptions, such as energy carrier prices, policy instruments and local infrastructures. The age of production units and their reinvestment cycles are considered the main restrictions on the dynamics of the transition. The results provide high spatial resolution to capture the spatial and temporal dynamics of industry transition under varying process and policy assumptions. The presented model and its results can be coupled with energy system models to assess the implications of site-specific industry transition on energy system related research questions. We conduct an exemplary case study for a transformation pathway of the European primary steel production.

AERA-MIP: emission pathways, remaining budgets, and carbon cycle dynamics compatible with 1.5 and 2 °C global warming stabilization

Abstract. While international climate policies now focus on limiting global warming to well below 2 °C or pursuing a 1.5 °C level of global warming, the climate modelling community has not provided an experimental design in which all Earth system models (ESMs) converge and stabilize at the same prescribed global warming levels. This gap hampers accurate estimations based on comprehensive ESMs of the carbon emission pathways and budgets needed to meet such agreed warming levels and of the associated climate impacts under temperature stabilization. Here, we apply the Adaptive Emission Reduction Approach (AERA) with ESMs to provide such simulations in which all models converge at 1.5 and 2.0 °C warming levels by adjusting their emissions over time. These emission-driven simulations provide a wide range of emission pathways and resulting atmospheric CO2 projections for a given warming level, uncovering uncertainty ranges that were previously missing in the traditional Coupled Model Intercomparison Project (CMIP) scenarios with prescribed greenhouse gas concentration pathways. Meeting the 1.5 °C warming level requires a 40 % (full model range: 7 % to 76 %) reduction in multi-model mean CO2-forcing-equivalent (CO2-fe) emissions from 2025 to 2030, a 98 % (57 % to 127 %) reduction from 2025 to 2050, and a stabilization at 1.0 (−1.7 to 2.9) PgC yr−1 from 2100 onward after the 1.5 °C global warming level is reached. Meeting the 2.0 °C warming level requires a 47 % (8 % to 92 %) reduction in multi-model mean CO2-fe emissions until 2050 and a stabilization at 1.7 (−1.5 to 2.7) PgC yr−1 from 2100 onward. The on-average positive emissions under stabilized global temperatures are the result of a decreasing transient climate response to cumulative CO2-fe emissions over time under stabilized global warming. This evolution is consistent with a slightly negative zero emissions commitment – initially assumed to be zero – and leads to an increase in the post-2025 CO2-fe emission budget by a factor of 2.2 (−0.8 to 6.9) by 2150 for the 1.5 °C warming level and a factor of 1.4 (0.9 to 2.4) for the 2.0 °C warming level compared to its first estimate in 2025. The median CO2-only carbon budget by 2150, relative to 2020, is 800 GtCO2 for the 1.5 °C warming level and 2250 GtCO2 for the 2.0 °C warming level. These median values exceed the median IPCC AR6 estimates by 60 % for the 1.5 °C warming level and 67 % for 2.0 °C. Some of the differences may be explained by the choice of the mitigation scenario for non-CO2 radiative agents. Our simulations highlight shifts in carbon uptake dynamics under stabilized temperature, such as a cessation of the carbon sinks in the North Atlantic and in tropical forests. On the other hand, the Southern Ocean remains a carbon sink centuries after temperatures stabilize. Overall, this new type of warming-level-based emission-driven simulation offers a more coherent assessment across climate models and opens up a wide range of possibilities for studying both the carbon cycle and climate impacts, such as extreme events, under climate stabilization.

CCOA‐AdaLS: Hybrid Beamforming Using Chaotic Chebyshev Aquila Optimization for mmWave Massive MIMO

ABSTRACTThis research aims to design hybrid analog and digital beamforming to improve the signal‐to‐noise ratio (SNR) and spectral efficiency (SE) of communication links. Considering the complexity and cost associated with fully connected multiple‐input multiple‐output (MIMO) communication models, a partially connected system model is adopted for the downlink millimeter‐wave (mmWave) communication model. The analog beamforming utilizes the adaptive search (AdaLS) algorithm to minimize interference and enhance user power, whereas digital beamforming is optimized using the proposed Chaotic Chebyshev Aquila Optimization (CCAO) algorithm. The CCAO algorithm integrates chaotic Chebyshev–based solution mapping with the conventional Aquila Optimization Algorithm to enhance exploration capability. The system model is illustrated, and the problem is formulated to maximize the signal‐to‐interference‐plus‐noise ratio (SINR) through the selection of the required signal at the receiver. The digital beamformer is designed using Lagrange's multiplier, and the analog beamformer is optimized using AdaLS. The proposed CCAO algorithm is detailed, incorporating chaotic dynamics to explore the solution space effectively. The research evaluates the performance of the proposed method against conventional approaches, showcasing improved normalized beam gain and SINR.

STUDY OF THE DYNAMICS OF A VIBRATION MACHINE CONSIDERING THE INFLUENCE OF THE PROCESSING MEDIUM

This paper presents the results of studying the dynamics of a vibration machine, takinginto account its interaction with the processing medium based on representing the medium as a continuoussystem. The application of the complex number method significantly simplifies the formation of equations ofmotion for the discrete-continuous system, which is a computational model of the vibration system "machinemedium."The study substantiates and develops a method for considering the mutual influence of the machineand the processing medium, based on representing the medium as a continuous system. The dynamicsof the vibration system "machine-medium" is studied, and oscillation parameters are determined withoutconsidering resistance forces. It is found that the overall motion of the vibration system is described by fourcomponents. The first three describe the natural oscillations of the system, of which the first two are determinedonly by initial conditions, and the third reflects the accompanying oscillations caused by the externalforce applied to the system. The last component defines the forced oscillations following the external force'schange law. This result shows that the oscillations of the vibration system are not strictly harmonic, which isconfirmed by the provided graphs. The dynamics of the "machine-medium" vibration system, consideringresistance forces, are studied, and analytical dependencies for determining oscillation amplitudes and natural and resonant frequencies are obtained. The results of the amplitude calculations of the vibration platform forvarious heights of compacted concrete mixtures reveal the influence of the resistance coefficient and theratio of oscillation frequency to the wave propagation speed in the mixture. Analysis of the obtained graphsshows that the resistance coefficient has a different effect on the amplitude of oscillations for different heightsof the compacting mixture. Certain mixture height zones of the vibration system "machine-medium" operatein a near-resonant mode. A significant influence on the amplitude is exerted by the wave propagation speed,which is included in the analytical formulas for determining the numerical values of the coefficients accountingfor reactive and active components of resistance.

Vehicle active suspension control considering parameter uncertainty and velocity variation

Due to the uncertainty in vehicle model parameters, the control performance of active suspensions often deteriorates, especially under extreme conditions such as high velocities and full loads. To address this challenge, this paper proposes a novel robust finite frequency H ∞ output feedback control strategy based on linear parameter varying (LPV) systems. First, we establish a new LPV model for the half-car active suspension control system, taking into account the uncertainties associated with vehicle velocity and sprung mass. Next, a robust finite frequency H ∞ output feedback controller is designed using a gain scheduling approach, formulated through a set of linear matrix inequalities (LMIs). Finally, simulation results demonstrate that the proposed velocity-dependent robust control strategy significantly reduces the root mean square (RMS) values of vertical acceleration by approximately 21% to 31% under various conditions of sprung mass and vehicle speed compared to traditional control methods.

RESEARCH OF ENERGY DISSIPATION ACCORDING TO THE DISCRETE-CONTINUUM MODEL OF A TWO-PLACE VIBRATION SYSTEM

In the work, the energy of the oscillation process was investigated according to the discrete model of the two-seat vibration system "working body of the machine - sealing medium". It was found that with a small dissipation of energy in the case of a force acting on one of the masses at a given amplitude of oscillations of this mass, the ratio of amplitudes of oscillations in the resonance mode is determined only by the parameters of the second mass and the ratio between the masses. The conditions for the effect of dissipation in the resonance mode at a frequency close to the partial frequency of the mass on which the external force acts are determined. At the same time, the difference between the inertial force acting on the second mass and the elastic force is not equal to zero. Therefore, the effect of dissipation is negligible. In the case of resonance at the frequency caused by the second mass, the role of scattering forces increases. The energy dissipation associated with the mass on which an external force acts has a significant effect on the frequency of the first resonance. The obtained research results are important information for the design of vibration machines of this class, including surface machines for laying concrete roads.

Smart energy: application of the photovoltaic system using genetic algorithms for decision making in industry 4.0

The growing demand for sustainable solutions and the digitalization of industrial processes have driven the adoption of photovoltaic systems and advanced decision-making technologies. In the context of Industry 4.0, where automation and artificial intelligence are fundamental, these systems stand out as a clean energy alternative, promoting savings and reducing pollutant emissions. This study aims to develop a photovoltaic energy control model that uses genetic algorithms to optimize energy efficiency in industrial environments, reducing costs and dependence on non-renewable sources. The methodology included the computational modeling of a photovoltaic system and the application of genetic algorithms to optimize parameters such as panel angle and operating hours, adapting the system in real time to variable consumption and generation conditions. The results showed that the use of genetic algorithms increased the system's efficiency by up to 20% compared to traditional methods, as well as minimizing consumption from the electricity grid at peak times. This study reinforces the importance of artificial intelligence in optimizing renewable resources, contributing to energy efficiency and sustainability in Industry 4.0.

Monarch butterfly optimization based energy management system for electric vehicle with interleaved landsman converter

The toxic emissions from combustion engine cars and businesses using fossil fuels have steadily increased over time in today's world of expanding needs and urbanization. Due to some advantages over vehicles with traditional engines, using electric vehicles is the most well-liked and successful alternative. The performance of electric cars is significantly influenced by an energy management system (EMS). In this study, a multifunctional power electronic interface capable of using two sources simultaneously throughout the charging process monarch butterfly optimization (MBO) based EMS for plug-in electric vehicles (PHEV) has been developed. The battery can be charged from the grid or solar photovoltaic (SPV), depending on the need. This study designs an electric vehicle (EV) charging system that uses an interleaved landsman converter followed by a flyback converter. As a result, the suggested converter helps in improving battery safety as well as user safety for the vehicle. The proposed control technique is simulated using system models created with MATLAB/Simulink. The outcomes of the simulation demonstrate that the devised technique offers better control in terms of vehicle stability. A number of simulations are run to evaluate the efficiency and performance of the proposed approach.

Sensitivity of Arctic marine heatwaves to half-a-degree increase in global warming: 10-fold frequency increase and 15-fold extreme intensity likelihood

Abstract We utilize the 50-member MPI-ESM-LR Earth System model to investigate the projected changes in Arctic marine heatwaves' (MHWs) characteristics caused by an additional 0.5°C increase in global warming, from 1.5°C to 2°C, with respect to pre-industrial levels. Our results indicate that this 0.5°C increase in global warming triggers an intensified response in both the Arctic's mean sea surface temperature (SST) and its variability. In a 2°C warmer world, one out of every four summer months would be warmer than the current climate. We detect a nonlinear increase in MHW intensity in a 2°C world, which is characterized by a break in slope occurring around the year 2042 ± 2 (across 50 ensemble members of the SSP5-8.5 scenario). At the estimated post-break dates, the intensity rate roughly doubles, leading to MHWs in a 2°C world with average cumulative heat intensity 100°C*days higher than in a 1.5°C world. Further results reveal that an extremely rare MHW with an intensity of 3.19°C, classified as a 1-in-100-year event in a 1.5°C world, is expected to transform into a 1-in-7-year event in a 2°C world. This transition signifies an approximately 15-fold increase in the likelihood of such events occurring due to a 0.5°C increase in global warming. Likewise, an uncommon event of years with 125 MHW days in a 1.5°C world is projected to become a 1-in-10-year event in a 2°C world, resulting in a 10-fold increase in occurrence probability. The main contributor to these changes is predominantly the rise in mean SST, with enhanced SST variability playing a minor role. These findings highlight that a 2°C world could lead to a substantial escalation of the frequency and intensity of MHWs in the Arctic compared to a 1.5°C world, transforming what are currently rare extreme events into more common events, with significant implications for global climate dynamics and the well-being of Arctic ecosystems and communities.

Efficient Finite-Difference Estimation of Second-Order Parametric Sensitivities for Stochastic Discrete Biochemical Systems

Biochemical reaction systems in a cell exhibit stochastic behaviour, owing to the unpredictable nature of the molecular interactions. The fluctuations at the molecular level may lead to a different behaviour than that predicted by the deterministic model of the reaction rate equations, when some reacting species have low population numbers. As a result, stochastic models are vital to accurately describe system dynamics. Sensitivity analysis is an important method for studying the influence of the variations in various parameters on the output of a biochemical model. We propose a finite-difference strategy for approximating second-order parametric sensitivities for stochastic discrete models of biochemically reacting systems. This strategy utilizes adaptive tau-leaping schemes and coupling of the perturbed and nominal processes for an efficient sensitivity estimation. The advantages of the new technique are demonstrated through its application to several biochemical system models with practical significance.

CSTR parameter identification and PID control optimization based on improved swarm intelligence algorithm

Abstract Aiming at the problems of strong coupling and large hysteresis that exist in the production process of a typical thermal object continuous stirred reactor (CSTR), which make it difficult to recognize the system model parameters and control the temperature, an improved firefly algorithm is proposed to recognize the system parameters, and its recognition accuracy is higher than that of the original firefly algorithm; and an improved sparrow search algorithm (BCGSSA) is used to control the temperature of the CSTR system by combining the BCGSSA algorithm with PID control, which significantly improves the dynamic performance of the CSTR system. Then an improved sparrow search algorithm (BCGSSA) is combined with PID control to control the temperature of the CSTR system, which significantly improves the dynamic performance of the CSTR, and the improved algorithm (BCGSSA) has good control accuracy and robustness.

Boundedness of the solution to a higher-dimensional triply haptotactic cross-diffusion system modeling oncolytic virotherapy

Boundedness of the solution to a higher-dimensional triply haptotactic cross-diffusion system modeling oncolytic virotherapy

Modeling of PV System Supported Intelligent Irrigation System in Iraq-Mosul Region

In this study, Matlab/Simulink is used to create a smart irrigation system that uses electricity generated by photovoltaic cells. In order to supply 90000 liters of water every irrigation period to meet the daily needs of farm irrigation, the system is intended for a rural location of Mosul city in the west. According to calculations, the necessary amount of water can be drawn from a 50 m water head well using a solar cell array that provides a submersible BLDC pump with a power of 1400 W. Employing smart irrigation demonstrated that the system's efficiency was increased and that it was more affordable than a standard pumping system. It is advised that Iraq, one of the solar-richest nations, employ smart irrigation systems, even though it now lacks energy.

Surface buoyancy control of millennial-scale variations in the Atlantic meridional ocean circulation

Abstract. Dansgaard–Oeschger (DO) events are a pervasive feature of glacial climates. It is widely accepted that the associated changes in climate, which are most pronounced in the North Atlantic region, are caused by abrupt changes in the strength and/or northward extent of the Atlantic meridional overturning circulation (AMOC), possibly originating from spontaneous transitions in the ocean–sea-ice–atmosphere system. Here we use an Earth system model that produces DO-like events to show that the climate conditions under which millennial-scale AMOC variations occur are controlled by the surface ocean buoyancy flux. In particular, we find that the present-day-like convection pattern with deep-water formation in the Labrador and Nordic seas becomes unstable when the buoyancy flux integrated over the northern North Atlantic turns from negative to positive. It is in the proximity of this point that the model produces transitions between different convection patterns associated with strong and weak AMOC states. The buoyancy flux depends on the surface freshwater and heat fluxes and on sea surface temperature through the temperature dependence of the thermal expansion coefficient of seawater. We find that larger ice sheets tend to stabilize convection by decreasing the net freshwater flux, while CO2-induced cooling decreases buoyancy loss and destabilizes convection. These results help to explain the conditions under which DO events appear and are a step towards an improved understanding of the mechanisms of abrupt climate changes.

Contrasting Southern Ocean Sea Level Responses to Surface Flux Changes in Eddy-Rich and Eddy-Parameterized Climate Model Configurations

Abstract The impact of ocean model resolution on sea level projections in the Southern Ocean is investigated using eddy-rich (ER) and eddy-parameterized configurations of the Max Planck Institute Earth System Model under the Shared Socioeconomic Pathway (SSP) 5-8.5 scenario. We employ the Flux-Anomaly Forced Model Intercomparison Project (FAFMIP) experiment—heat, stress, and freshwater perturbations—at both resolutions to pinpoint the sources of these differences. South of 55°S, we found that the changes in thermosteric and halosteric sea levels vary substantially between resolutions due to different responses to freshwater perturbations. In the eddy-parameterized model, the resulting increase in stratification suppresses the mixing of salt and heat from the Circumpolar Deep Water with surface layers. These cause differences in the response of surface fluxes and meridional transports yielding an increase in thermosteric sea levels and a decrease in halosteric sea levels. In the eddy-rich configuration, the main driver of eddy-induced warming and salinification between 40° and 44°S is wind stress perturbations. The efficiency of direct eddy effects in ER is restricted to small areas such as the Agulhas Retroflection, the Brazil–Malvinas confluence zone, the Tasman Sea, and, to some extent, the Antarctic Circumpolar Current (ACC). Contrary to expectations, ACC transport increases in the eddy-rich model while decreasing in the eddy-parameterized model under the SSP5-8.5 scenario. FAFMIP results reveal that this decrease is a result of the overcompensation of wind-induced changes by freshwater flux forcing. These results underscore the critical importance of high-resolution models for capturing the processes in sea level projections in the Southern Ocean and beyond. Significance Statement We studied how ocean model resolution affects sea level projections in the Southern Ocean using Max Planck Institute Earth System Model simulations. Higher-resolution models provide a more accurate representation of ocean circulation and its response to changing forcings. We examined how surface heat, momentum, and water fluxes, both separately and combined, shape ocean dynamics. In a strong global warming scenario, significant differences in steric sea level change were observed south of 55°S between the model that simulates eddies and the one that has their effects parameterized. The response to surface freshwater forcing is the primary cause of these differences. Our findings emphasize the critical role of ocean model resolution in accurately understanding and predicting future sea level changes, which is essential for effectively addressing our needs for adaptation.

Experimental Data-Driven Estimation of Impulse Response in Audio Systems Using Parametric and Non-Parametric Methods

The impulse response is a fundamental tool for characterizing linear time-invariant (LTI) systems, enabling the derivation of a mathematical model that accurately describes system dynamics under arbitrary input conditions. This study used experimental data to estimate the impulse response of an audio system—comprising an amplifier, a speaker, a room, and a microphone. Four methods were employed: two parametric and two non-parametric approaches, applied in both the time and frequency domains. The methods were evaluated quantitatively using the Root Mean Square Error (RMSE) metric and qualitatively through a perceptual analysis with six participants. The parametric frequency-domain method achieved the best perceptual results, with 75% of participants rating the output as good. While this method exhibited slightly higher RMSE compared to other techniques, its low filter order (8) resulted in superior computational efficiency. The findings highlight that perceptual alignment often diverges from purely mathematical error minimization. Real-time implementation of the selected impulse response further demonstrated its practical application in audio processing systems. This research bridges quantitative metrics and human auditory perception, emphasizing the need for balanced decision-making in audio system modeling. The results contribute to advancing data-driven methodologies in acoustics, offering insights into both experimental design and computational efficiency