Elaborate Models Research Articles

Retrofitted CCS technologies enhance economy, security, and equity in achieving carbon zero in power sector

Retrofitting existing fossil fuel power plants with carbon capture and storage (CCS) technology could reduce carbon emissions while avoiding stranded asset, which will be important in facilitating a just transition of the global power sector. Although some studies have explored the cost-effectiveness and abatement potential of retrofitted CCS technologies, elaborate system modeling of full-chain retrofitted CCS technologies considering multiple technology types requires further research. Here, we developed an hourly-resolution intertemporal dynamic power system optimization model that elaborately considers three retrofitted CCS technologies for coal-fired and gas-fired power plant in addition to eleven power generation technologies and two energy storage technologies and applied it to evaluate the role of retrofitted CCS technologies in achieving carbon neutrality in China's power sector. The results show that, compared with no retrofitted CCS power system, the high development of retrofitted CCS can reduce the future installed capacity and power generation demand of China's power sector by up to 605GW or 10.5 % (in 2040) and 0.17 PWh or 0.9 % (in 2060), respectively. The cumulative system decarbonization costs and electricity supply costs will decrease 6.2–8.2 % and 2.1–2.6 % by 2060, respectively, due to the savings in related costs of newly built plants and reduction in potential power shortages, in addition to avoidance of large coal-fired power stranded assets. The developed model could be a reference for other countries, and in China and perhaps in other economies with coal-dominant power systems, policies advocating the development of retrofitted CCS should be strengthened.

Experiments and FE Modeling on the Seismic Behavior of Partially Precast Steel-Reinforced Concrete Squat Walls

This paper proposed an innovative precast steel-reinforced concrete (PPSRC) squat wall to simplify on-site construction. In PPSRC squat shear walls, the hollowly precast RC wall panel can be assembled on-site through the pre-erected steel shapes, and the boundary cores will be filled using fresh concrete together with the slab system. The seismic performance of PPSRC squat walls, influenced by different construction techniques (cast-in-place vs. precast) and steel ratios, was examined through pseudo-static experiments on three specimens. Some key performance indicators, including hysteretic behavior, skeleton curves, stiffness degradation, energy dissipation, and load-carrying capacity, were analyzed in detail. The test results indicated that all the PPSRC squat walls failed in typical shear failure, and no significant slippage between the precast and fresh concrete sections was observed during the loading process, indicating that the composite action could be fully achieved via the novel throat connectors. In addition, the PPSRC squat walls could achieve comparable seismic performance compared with that of cast-in-place SRC shear walls (the peak load of the PPSRC squat wall only increased by 0.26% compared with the control specimen), and the load-carrying capacity and deformability could be enhanced by increasing the steel ratio in the boundary elements. Finally, an elaborate finite element model was developed and validated using ABAQUS software. The parametric analysis of the concrete strengths of precast and cast-in-place parts and the axial load was conducted further to investigate the seismic performance of PPSRC squat walls.

Validation of a new road and contact model for vehicle–soft soil terrain interaction through an elaborate modelling of the FED-Alpha vehicle

Employing multibody dynamic simulations with semi-empirical tyre models is widely recognised as a cost-effective approach. A recent development introduces a novel road and tyre–soil contact model that is not only swift and memory-efficient but also addresses limitations in classical semi-empirical models. This study conducts a thorough validation of the new road and contact model by creating a detailed multibody model of the four-wheeled vehicle, Fuel Efficiency Demonstrator – Alpha, integral to NATO’s Next-Generation reference mobility model. The comprehensive model encompasses the chassis, suspension, tyres, engine, transmission and various other components. Through simulations of various driving scenarios, accounting for complex terrain geometries, spatially varying soil properties and multi-pass phenomena, the model’s performance is evaluated. The simulation results are compared with physical measurements, providing a detailed assessment of the tyre–soil model’s predictive accuracy for wheeled vehicle mobility on deformable terrains.

Analytical solution of susceptible-infected-recovered models on homogeneous networks.

The ability to actually implement epidemic models is a crucial stake for public institutions, as they may be overtaken by the increasing complexity of current models and sometimes tend to revert to less elaborate models such as the susceptible-infected-recovered (SIR) model. In our work, we study a simple epidemic propagation model, called SIR-k, which is based on a homogeneous network of degree k, where each individual has the same number k of neighbors. This model represents a refined version of the basic SIR which assumes a completely homogeneous population. We show that nevertheless, analytical expressions, simpler and richer than the ones existing for the SIR model, can be derived for this SIR-k model. In particular, we obtain an exact implicit analytical solution for any k, from which quantities such as the epidemic threshold or the total number of agents infected during the epidemic can be obtained. We furthermore obtain simple exact explicit solutions for small ks, and in the large k limit we find a new formulation of the analytical solution of the basic SIR model, which comes with new insights.

Vertical specialization, global expansion of supply chain, and convergence

Abstract In this paper, we construct an elaborate general equilibrium model with a continuum of production fragments for an intermediate good, then incorporate it in a growth model to address the effects of global production fragmentation, vertical specialization, and trade on growth and inequality for a small developing country. Among other things, we show that a small developing economy grows faster than the rest of the world as a result of global fragmentation and trade in intermediates if it is skilled-labor scarce. We further address the effects of such a trade opening on wage inequality.

Modeling human activity-related spread of the spotted lanternfly (Lycorma delicatula) in the US.

The spotted lanternfly (Lycorma delicatula) has recently spread from its native range to several other countries and forecasts predict that it may become a global invasive pest. In particular, since its confirmed presence in the United States in 2014 it has established itself as a major invasive pest in the Mid-Atlantic region where it is damaging both naturally occurring and commercially important farmed plants. Quarantine zones have been introduced to contain the infestation, but the spread to new areas continues. At present the pathways and drivers of spread are not well-understood. In particular, several human activity related factors have been proposed to contribute to the spread; however, which features of the current spread can be attributed to these factors remains unclear. Here we collect county level data on infestation status and four specific human activity related factors and use statistical methods to determine whether there is evidence for an association between the factors and infestation. Then we construct a network model based on the factors found to be associated with infestation and use it to simulate local spread. We find that the model reproduces key features of the spread 2014 to 2021. In particular, the growth of the main infestation region and the opening of spread corridors in the westward and southwestern directions is consistent with data and the model accurately forecasts the correct infestation status at the county level in 2021 with 81% accuracy. We then use the model to forecast the spread up to 2025 in a larger region. Given that this model is based on a few human activity related factors that can be targeted, it may prove useful to incorporate it into more elaborate predictive forecasting models and in informing management efforts focused on interstate highway transport and garden centers in the US and potentially for current and future invasions elsewhere globally.

Resilient multi-objective mission planning for UAV formation: A unified framework integrating task pre- and re-assignment

Combat effectiveness of unmanned aerial vehicle (UAV) formations can be severely affected by the mission execution reliability. During the practical execution phase, there are inevitable risks where UAVs being destroyed or targets failed to be executed. To improve the mission reliability, a resilient mission planning framework integrates task pre- and re-assignment modules is developed in this paper. In the task pre-assignment phase, to guarantee the mission reliability, probability constraints regarding the minimum mission success rate are imposed to establish a multi-objective optimization model. And an improved genetic algorithm with the multi-population mechanism and specifically designed evolutionary operators is used for efficient solution. As in the task-reassignment phase, possible trigger events are first analyzed. A real-time contract net protocol-based algorithm is then proposed to address the corresponding emergency scenario. And the dual objective used in the former phase is adapted into a single objective to keep a consistent combat intention. Three cases of different scales demonstrate that the two modules cooperate well with each other. On the one hand, the pre-assignment module can generate high-reliability mission schedules as an elaborate mathematical model is introduced. On the other hand, the re-assignment module can efficiently respond to various emergencies and adjust the original schedule within a millisecond. The corresponding animation is accessible at bilibili.com/video/BV12t421w7EE for better illustration.

A light-fueled self-rolling unicycle with a liquid crystal elastomer rod engine

Active materials hold substantial potential for self-sustaining motion, however, the dependency on movable light or extensive area illumination in current structures often restricts the design and practical deployment of such active machines. In this study, we report a novel zero-energy-mode, self-rolling unicycle equipped with a liquid crystal elastomer rod engine, which fueled by a fixed, small-area light source. By employing an elaborate dynamic model for liquid crystal elastomer, we derive the lateral curvature of the liquid crystal elastomer rod and the driving moment necessary for the unicycle's self-rolling. The study results demonstrate that the self-rolling of the unicycle is driven by a moment induced by the shift in the center of gravity of the curved liquid crystal elastomer rod. Our numerical simulations indicate the presence of a supercritical Hopf bifurcation point delineating the transition between the self-rolling mode and the static mode within the unicycle system. Additionally, the rolling velocity of the unicycle relies on a handful of key system parameters, notably including light intensity, light penetration depth, length of the liquid crystal elastomer rod, rolling friction coefficient, total mass of the unicycle, and wheel radius of the unicycle. The experimental validation of a self-rolling unicycle with zero-energy-mode has been conducted. The self-rolling unicycle constructed in this paper has excellent characteristics of simple structure, zero-energy-mode, horizontal constant light source, and small area light source, providing valuable insights for the utilization of photoresponsive liquid crystal elastomer rods in soft robotics, medical devices, energy harvesting systems, and actuators.

Study on the shear mechanism of horizontal joints within UVDR precast shear wall by quasi-static test and finite element analysis

This research introduced a novel precast shear wall designed to address prevalent challenges encountered in precast shear wall structures, such as issues with difficult installation, high expenses, and the uncertain quality of grouting by eliminating the connection of the vertical distributing bars. Early experimental verification of this novel structural system showcased favourable results concerning its seismic performance. Building upon this groundwork, the paper presented an elaborate horizontal joint deformation model, demonstrating its precise analysis of damage modes and interface slip in the novel structural system with high reliability. Furthermore, in an effort to enhance the damage mode of this novel precast shear wall under conditions of low axial compression ratio, the study introduced and evaluated two improvement measures through finite element parametric analysis. The findings indicated a noticeable enhancement in capacity and a simultaneous reduction in interface slip with the proposed improvement measures. Additionally, the section steel measures were more effective than the additional bars measures.

Application of a single-equation SARIMA model for short-term conditional forecast (projection) of CPI price dynamics in Poland

The aim of the article is to construct an optimal SARIMA model for short-term conditional forecasting (projection) of the dynamics of prices expressed by the Consumer Price Index (CPI), as understood within the extended Box-Jenkins procedure. The construction of such a forecast aims to influence, through the expectations channel, the institutional trust of society in monetary authorities and assess the effectiveness of achieving the monetary policy goal within the framework of the democratic responsibility of the decision-making body of the National Bank of Poland – the Monetary Policy Council. The selection of the optimal SARIMA model was carried out using an iterative method within the Box-Jenkins procedure, with the goal of reducing the systematic bias of estimators – coherence with empirical data. The analysis was conducted on compiled secondary data of the monthly Consumer Price Index for goods and services from the Central Statistical Office for the years 2010-2023 (on a monthly basis). Results show that the short-term forecast demonstrated accuracy within a specified confidence interval. The application of the SARIMA model serves as a useful methodological tool for constructing elaborate DSGE models (for example, the NECMOD model) using procedures such as SEM (System for Averaging Models) from Norges Bank.

Environmental information-assisted intelligent fusion localization for vehicles in urban area

In this paper, we propose a localization methodology aimed at improving accuracy through two primary aspects: environment information assisting fusion algorithm and the elaborate localization errors prediction. First, the environment-assisted particle filter (EA-PF) is proposed by enhancing the particle filter algorithm with perceived environmental feature information, which is reflected by the rasterized map constructed based on LiDAR sensors. Then, an elaborate localization error prediction model is designed to accurately forecast Inertial Navigation System (INS) errors. The model comprises the noise characteristic feature extraction and encoding network and multi-feature error prediction network. The localization errors in the EA-PF output can be further compensated. The proposed localization methodology can be divided into two operational modes based on the availability of Global Navigation Satellite System (GNSS) signal: GNSS-available and GNSS-unavailable modes. In the GNSS-available mode, the EA-PF algorithm yields precise localization results, while in the GNSS-unavailable modes, the elaborate localization error prediction model can effectively predict INS errors and provide the compensation for the EA-PF. To validate the effectiveness of the proposed methodology, relevant test experiments were performed using the KITTI dataset. The experimental validation results demonstrate the feasibility and effectiveness of the proposed methodology. The experimental results demonstrate that the EA-PF outperforms traditional PF methods by improving the root mean square error (RMS.) by 32.70 % on average during 90 s GNSS outages. Moreover, the proposed localization methodology achieves 2.30 m RMS error on average during 90 s GNSS outage.

Musculoskeletal spine modeling in large patient cohorts: how morphological individualization affects lumbar load estimation.

Introduction: Achieving an adequate level of detail is a crucial part of any modeling process. Thus, oversimplification of complex systems can lead to overestimation, underestimation, and general bias of effects, while elaborate models run the risk of losing validity due to the uncontrolled interaction of multiple influencing factors and error propagation. Methods: We used a validated pipeline for the automated generation of multi-body models of the trunk to create 279 models based on CT data from 93 patients to investigate how different degrees of individualization affect the observed effects of different morphological characteristics on lumbar loads. Specifically, individual parameters related to spinal morphology (thoracic kyphosis (TK), lumbar lordosis (LL), and torso height (TH)), as well as torso weight (TW) and distribution, were fully or partly considered in the respective models according to their degree of individualization, and the effect strengths of these parameters on spinal loading were compared between semi- and highly individualized models. T-distributed stochastic neighbor embedding (T-SNE) analysis was performed for overarching pattern recognition and multiple regression analyses to evaluate changes in occurring effects and significance. Results: We were able to identify significant effects (p < 0.05) of various morphological parameters on lumbar loads in models with different degrees of individualization. Torso weight and lumbar lordosis showed the strongest effects on compression (β ≈ 0.9) and anterior-posterior shear forces (β ≈ 0.7), respectively. We could further show that the effect strength of individual parameters tended to decrease if more individual characteristics were included in the models. Discussion: The induced variability due to model individualization could only partly be explained by simple morphological parameters. Our study shows that model simplification can lead to an emphasis on individual effects, which needs to be critically assessed with regard to in vivo complexity. At the same time, we demonstrated that individualized models representing a population-based cohort are still able to identify relevant influences on spinal loading while considering a variety of influencing factors and their interactions.

Digital twin-driven discriminative graph learning networks for cross-domain bearing fault recognition

Rolling bearing fault diagnosis holds paramount significance in industrial field, as it is pivotal for ensuring the reliability, safety, and economic viability of mechanical systems. Most of traditional data-driven fault diagnosis methods rely on the availability of pre-collected data sets (containing various failure modes) as training data. Regrettably, such datasets may not always be easy to collect, particularly in critical industrial settings. Consequently, the applicability of data-driven fault diagnostic methods across diverse applications is impeded. In response to this challenge, this research introduces a digital twin-empowered discriminative graph learning network (DT-DGLN) for bearing fault diagnostics with unlabeled industrial data. The primary improvements of this study are twofold: (1) The creation of an elaborate and meticulous digital twin model for bearings. This model encompasses multi-scale Kinetic simulations of the operational parameters of the bearing and relies exclusively on the architectural attributes of the bearing and magnitude of the failure to deduce the vibratory system’s reaction. (2) The establishment of a domain adaption-based framework based on discriminative graph learning strategy that facilitates the transfer of known intelligence from simulated signals to real signals. This framework enables efficient fault recognition in cases where knowledge is limited. Rigorous experiments have been conducted to validate the effectiveness of the proposed approach.

Validation of the thermo-chemical approach to modelling of CO2 conversion in sub-atmospheric pressure microwave gas discharges

The CO 2→ CO + 12 O2 conversion experiment [D’Isa et al 2020 Plasma Sources Sci. Technol. 29 105009] has been compared with thermo-chemical calculations. The experiment is a 2.45 GHz plasma torch with a straight channel in the effluent. The 1.5D model of the CO2/CO/O2/O/C mixture without turbulent transport has been applied with plasma acting only as a prescribed heat source. The parameter range covered is the specific energy input (SEI) 0.3–5 eV/molecule at pressure p = 0.9 bar, and SEI = 0.6–2 eV/molecule at p = 0.5, 0.2 bar. The calculated conversion χ is always close to the experimental values. At the same time, the calculated temperatures T deviate significantly from the experiment, especially for p = 0.2 bar. The calculated T were also found to be sensitive with respect to the uncertain model parameters, but χ is not sensitive. The net conversion in the model is driven to a large extent by the radial diffusion of CO and O from the hot core towards the wall and steep radial temperature gradients. The main factor that reduces the energy efficiency is the re-oxidation of CO at the edge of the hot plasma region and downstream. The 1.5D approximation applied has the principle limitation that the impact of a realistic bulk flow field on the chemical process cannot be studied. Hence, the results must be considered preliminary and have to be confirmed with a more elaborate and accurate model of the vortex-stabilized flow inside the reactor.

Study of the effect of heat temperature on the chemical changes and hygroscopicity of eucalyptus wood by FT-IR and prediction of mechanical properties by the MLR regression method

This study describes the effect of heat treatment on some physical, chemical, and mechanical properties of Eucalyptus Camaldulensis (EC) wood at different temperatures and treatment times (200 °C–260 °C for 5, 60, and 90 min). The evaluation of hygroscopic properties was determined by relative humidity, mass loss, dimensional stability tests, and density. The results showed that the heat treatment leads to an increase in mass loss of 5.2 %–11.9 % at 200 °C. The density changed significantly for this studied species as well as the dimensional stabilization. Chemical changes in wood structure were assessed by Fourier Transform Infrared Spectroscopy.To verify the validity of the superposition “Mass loss-Density-water absorption” on the mechanical properties (modulus of elasticity (MOE) and modulus of rupture (MOR)) during heat treatment, we have developed a mathematical model based on Multiple Linear Regression (MLR), in order to establish a relationship between the independent parameters and the dependent parameters (MOE and MOR). The evaluation of the quality of the models developed was based on several statistical tools, namely R = 0.99, R2 = 0.99, R2adj = 0.98, and F = 132.33. The results demonstrated that elaborate models of mechanical properties have a high predictive capacity (MOR and MOE). The wood’s carbohydrates (particularly hemicelluloses) are then degraded during the heat treatment. The % of carbon increases from 47.8 to 49.8 %, which is proportional to mass loss, while the % of oxygen decreases by 46.1 %, which is inversely proportional to mass loss. Furthermore, FTIR analysis revealed that the effect of heat-treated wood chemical changes was related to the hydroxyl OH function of cellulose, functional groups, and aromatic system of lignin. In conclusion, the results demonstrated that at 200 °C, heat treatment caused a 5.2–11.9 % increase in mass loss; dimensional stability and density underwent considerable changes. FTIR spectroscopy confirmed the chemical changes in the wood structure during heat treatment. Furthermore, the “MLR” mathematical model showed that density contributed to the increase in MOR and MOE properties, while water absorption and mass loss contributed to the decrease in MOR and MOE properties. Finally, the % of oxygen decreased by 46.1 %, which is inversely proportional to the loss of mass, and the % of carbon increased from 47.8 % to 49.8 %, which is proportional to the loss of mass.

Estimating receptive fields of simple and complex cells in early visual cortex: A convolutional neural network model with parameterized rectification.

Neurons in the primary visual cortex respond selectively to simple features of visual stimuli, such as orientation and spatial frequency. Simple cells, which have phase-sensitive responses, can be modeled by a single receptive field filter in a linear-nonlinear model. However, it is challenging to analyze phase-invariant complex cells, which require more elaborate models having a combination of nonlinear subunits. Estimating parameters of these models is made additionally more difficult by cortical neurons' trial-to-trial response variability. We develop a simple convolutional neural network method to estimate receptive field models for both simple and complex visual cortex cells from their responses to natural images. The model consists of a spatiotemporal filter, a parameterized rectifier unit (PReLU), and a two-dimensional Gaussian "map" of the receptive field envelope. A single model parameter determines the simple vs. complex nature of the receptive field, capturing complex cell responses as a summation of homogeneous subunits, and collapsing to a linear-nonlinear model for simple type cells. The convolutional method predicts simple and complex cell responses to natural image stimuli as well as grating tuning curves. The fitted models yield a continuum of values for the PReLU parameter across the sampled neurons, showing that the simple/complex nature of cells can vary in a continuous manner. We demonstrate that complex-like cells respond less reliably than simple-like cells. However, compensation for this unreliability with noise ceiling analysis reveals predictive performance for complex cells proportionately closer to that for simple cells. Most spatial receptive field structures are well fit by Gabor functions, whose parameters confirm well-known properties of cat A17/18 receptive fields.

조직 구성원의 연령 다양성과 기업 성과 및 이직률 관계 연구: 인적자본 유입의 조절효과 검증

This study investigates the influence of age diversity on firm outcomes (i.e. firm performance and employee turnover rates) with the moderating effects of human capital inflow. The Categorization Elaboration Model (CEM) suggests that the key process that enables positive effects of diversity depends on the task-related information elaboration process, while socially categorizing each other hinders this process, resulting in the negative influence of diversity. Based on this model, we suggest that age diversity may have mixed effects on firm performance and employee turnover rates, suggesting both positive and negative effects. In line with human capital literature, we also argue that human capital inflow can determine the functional or dysfunctional effects of age diversity on firm performance and employee turnover rates. In particular, we argue that an increase in the influx of human capital leads to the formation of relationships, which in turn suppresses existing social categorization processes and enhances the positive effects of diversity. Using a sample of 283 Korean firms from Korean Women Manager Panel (KWMP) data, our findings show that age diversity is positively related to firm performance and negatively associated with employee turnover rates, suggesting the functional role of age diversity. Additionally, we find that human capital inflow significantly strengthens the functional effects of age diversity. Our study contributes to the literature on diversity and human capital by demonstrating the joint effects of diversity and human capital management.

A coarse-grained Poisson–Nernst–Planck model for polyelectrolyte-modified nanofluidic diodes

Abstract Polyelectrolyte (PE)-modified synthetic nanopores have gained substantial research attention because molecular modification promotes ion gating and rectification. However, theoretical research on PE-modified nanopores is relatively scarce because it is difficult to establish an elaborate model for PEs, and it accordingly causes a trade-off between the computational resources needed and the accuracy. Therefore, an appropriate simulation method for the PE-modified nanopore is in high demand and still an enormous challenge. Herein, we report the simulation result of ion transport through PE-modified nanopores through a coarse-grained Poisson–Nernst–Planck method. By modeling the stuffed PE molecules as PE particles in a well-established continuum model, adequate computational accuracy can be achieved with acceptable computational cost. Based on this model, we study the ion transport in PE-modified nanofluidic diodes and reveal the PE around ion selectivity, which can explain the previous experimental works. Intriguingly, we found that the ion enrichment state in the nanofluidic diode is sensitive to steric hindrance and charge distribution near the heterojunction region. This property is critical for the ion transport behavior in the PE-modified nanofluidic diodes. Based on this property, we predict a heterogeneous structure that can realize the single molecule response to charged analytes. These findings provide insights for understanding the ion transport in PE-modified nanofluidic systems and bring inspiration to the design and optimization of high-performance chemical sensors.

Establishing a Material Description Table to Optimize Mandarin Proficiency for Indonesian Learners: An Applied Contrastive Analysis Perspective

Abstract. This study aims to elucidate the most contrasting differences between Mandarin and Indonesian language rules. Based on the analysis results, it is hoped that this can serve as a foundation for creating a learning media, namely a descriptive material table. The study reveals that the most contrasting rule lies in the Indonesian language auxiliary number words, which have no more than 30 auxiliary number words, whereas in Mandarin, or known as 量, there are over 200 such words. Additionally, there is a rule for constructing past tense sentences. In Indonesian, a word meaning “finished” is used to indicate the past, contrasting with Mandarin, which employs the particle “了”, which can also have different meanings according to different rules. Furthermore, there is a Numeral system rule where Indonesian simply translates numbers into letters, while Mandarin has specific and detailed rules; this clearly presents a contrasting difference. There is also a rule on reduplication, or in Mandarin termed 重叠, where the quantity of rules between the two languages is almost balanced, yet the functions of usage are vastly contrasting. Lastly, there is the rule of “离合词”, a Mandarin language rule that has no equivalent in Indonesian. The method employed is a mixed-method approach, along with a narrative elaboration model based on quantitative differences in the amount of data between Mandarin and Indonesian languages, which are then expounded upon qualitatively. The data source used is the Confucius Institute (孔子学院) recommended books: 标准教程 Standard Course HSK (Hanyu Shuping Kaoshi / Chinese Proficiency Tes) 1 - 6 下 (Part 2). Data analysis techniques include identification, classification, and presentation of the results of data analysis. The results of the research and presentation show that there is a big gap between Mandarin and Bahasa Indonesia linguistic tools, with each of the rules that bind it. Therefore, it is necessary to develop a learning media to reduce this gap.Keywords: Material description table, Applied Contrastive Analysis, Mandarin, Indonesian Learners.

Design and Analysis Models with PID and PID Fuzzy Controllers for Six-Phase Drive

Due to their reliability, design and analysis models with PID and PID fuzzy controllers for six-phase drive are being applied in new areas in various industries, including transportation. First, the development of any system with multiphase motors requires an elaborate model to define the control mode and controllers. The modeling of a control system for six-phase drive is based on its conventional d-q mathematical model and indirect field-oriented control. In this study, a Simulink six-phase drive model is designed with indirect field-oriented control and simulated with two types of fuzzy controller, PID and PID fuzzy. The simulation results are presented and analyzed; these results reflect the step response and performance at the provided speed reference law while keeping the load application at a constant speed. A fuzzy controller with 49 rules is considered and applied. With field-oriented control and a well-tuned PID controller, the six-phase electric drive has good step response specifications: a short settling time when starting without a load, no overshoot in the step response, small size, and a slight decrease in speed when loaded. The system employing a PID fuzzy controller shows slightly better results in response to the application of torque: the decrease in speed is eliminated more quickly. The simulation results were tabulated with the PID and with the results of previous research that rearranged some models to only operate in the classical controller mode. The simulation results indicate the robustness to disturbance of both the systems with six-phase drive and provide high-quality transient specifications at the provided reference speed.