System Model Research Articles

The model of decision support system using hybrid method and actual weighting for the study program ranking

Nowadays the good or bad study program can be seen from the accreditation rank that it obtains from the institution of college accreditation. However, it is frequently found at college that there are some study programs that have the same accreditation. This encourages the college to do another approach which can do this study program ranking from a different point of view. This research developed a model of decision support system to do ranking towards 25 study programs existed in the environment of Sriwijaya State Polytechnic. Hybrid method employed the combination of analytical hierarchy process (AHP) and simple additive weighting (SAW) to do the ranking. Actual weighting model was used in the calculation based on the fact obtained in each study program, and in line with the criteria which had been determined. As many as 7 relevant criteria and 21 sub criteria were used in this model. The results of this research showed that the model which had been developed can give recommendation in the form of study program ranking with actual condition based on the data attached to each study program.

Discharge variations of qanat near an ephemeral stream

Ephemeral streams are the most important sources of groundwater recharge in arid and semi-arid regions of the world. To extract this replenishable water, most of the time, qanats are constructed parallel to the ephemeral streams. Analytical modeling of a stream-qanat system can provide an effective tool for managing such a system in a timely fashion and, thus, is commonly applied in the hydrological communities. However, the available analytical solutions of discharge variation of qanat only consider the distributed recharge from rainfall and cannot be used to investigate the effects of geometric parameters of stream and hydraulic parameters of aquifer on the discharge of qanat near an ephemeral stream. Thus, the goal of this work is to obtain a semi-analytical solution of qanat discharge due to recharge from an ephemeral stream of an arbitrary shape in a three-dimensional anisotropic homogeneous unconfined aquifer. The solution is obtained via Laplace and Fourier transformations along with the principle of superposition. The results of this study are presented in the form of qanat discharge-time curves. The influences of the qanat length, shape, and recharge distribution along the ephemeral stream are explored in great detail. The effects of the hydraulic parameters of the aquifer on the discharge of qanat are also investigated. The results of this study can be used to predict the discharge of qanat due to recharge from an ephemeral stream, to calculate the optimum length of a qanat for maximum extraction of the replenished water, to estimate the adequate recharge from the ephemeral stream and the hydraulic parameters of aquifer using the discharge data of qanat, among other applications.

Overbounding Ifree errors based on bayesian Gaussian mixture model for ground-based augmentation system

A dual-frequency measurement is employed in conjunction with an innovative Ifree filtering technique for mitigating the primary sources of Ifree influence on ground-based augmentation systems (GBAS) to safeguard the reliability of GBAS. The protective level achieved through the conventional Gaussian overbounding approach that are considered as much conventional technique. This adherence to tradition results in decreased reliability and a higher likelihood of false alarms. In contrast, the utilization of the Ifree algorithm contributes to reducing errors associated with dual-frequency measurements. This paper proposes the overbounding process according to Bayesian Gaussian mixture model (GMM) for maintaining Ifree-based GBAS range error. The Bayesian GMM is utilized for single-frequency model errors to examine the ambiguity estimations. The Monte Carlo (MC) simulation is established for defining estimated GMM assurance level accuracy which is attained through the general estimation method. Then, the last Bayesian GMM which is utilized for overbounding Ifree error distribution is investigated. According to the property of convolution invariance, the vertical protection in position field is determined without presenting difficult numerical calculations.

Enhanced Modeling of Wireless Power Transfer System With Battery Load

Enhanced Modeling of Wireless Power Transfer System With Battery Load

Experimental investigation of the small-scale prototype of the dew point water recovery system

The article presents experimental investigation of the Dew Point Water Recovery (DPWR) system designed for clean water recovery using the dew point cooling phenomenon. The technology is a novel distillation process which allows for highly efficient water recovery. It is a first distillation technology which allows for water distillation through direct contact with atmospheric air. The authors designed, built, and tested an original prototype of a laboratory-scale DPWR system to verify its basic operating principles and perform performance analysis. It was shown that on average 10 × 10−6 m3 of clean water could be recovered from each cubic meter of the supply air with an average GOR of 1.9. Although the operation of the DPWR system requires electricity to drive fans, pumps and other associated equipment, the actual driving force of the system is thermal energy. The average thermal energy demand of the DPWR system was 370 kWh/m3, and more than half of this energy was based on the thermodynamic potential of the supply air. Based on experimental data, the mathematical model of the DPWR system was verified and it was found that the average relative discrepancy in predicting air temperature did not exceed 8%, and in predicting air humidity: 5%. The conducted experiments confirmed that the proposed DPWR system is feasible and has the potential to fill the technological gap in terms of a low-energy and easily accessible water recovery method.

Environmental impact assessment for a meta-model-based food-energy-water-nexus system

A growing global population uses essential resources such as food, energy, and water to drive economic growth. However, population growth, urbanization, and human activities strain these limited resources, resulting in serious harm, particularly from global warming and climate change. Sustainable resource allocation in the Nexus system is required for improved resource security. This issue is essential since it impacts resource management, sustainability, and the environment. This research proposes developing an integrated assessment model (IAM) for the FEW-N system. The system undergoes a comprehensive evaluation that considers sustainability, economics, social aspects, and the environment. This evaluation combines an environmental impact assessment with a meta-model-based approach to the FEW-N system. A multi-objective optimization model is utilized to optimize the system with three key targets: maximum economic benefit, positive environmental impact, and least negative environmental impact. The findings show significant improvements in resource security within a community-level FEW-N system, with the Nexus system sectors contributing to both food and energy security. Greenhouse farming, rainfed farming, and irrigated farming supply 55.6%, 11.9%, and 32.5% of food security, respectively. Similarly, bioenergy, solar/wind hybrid renewable energy, and hydropower contribute 52.7%, 40.1%, and 7.2% of total energy security, respectively. The FEW-N system optimizes resource allocation for sustainability, reliability, and efficiency while emphasizing the specific contributions of diverse sectors to food and energy security. These findings provide critical insights for making educated resource allocation policy decisions in South Africa, emphasizing balancing societal, economic, and environmental factors. The study has increased the evidence base for the FEW-N environmental impact assessment framework to address resource allocation issues.

Robust optimal control for uncertain wheeled mobile robot based on reinforcement learning: ADP approach

This paper presents a robust optimal control approach for the wheel mobile robot system, which considers the effects of external disturbances, uncertainties, and wheel slipping. The proposed method utilizes an adaptive dynamic programming (ADP) technique in conjunction with a disturbance observer. Initially, the system's state space model is formulated through the utilization of kinematic and dynamic models. Subsequently, the ADP method is employed to establish an online adaptive optimal controller, which solely relies on a single neural network for the purpose of function approximation. The utilization of the disturbance observer in conjunction with the compensation controller serves to alleviate the effects of disturbances. The Lyapunov theorem establishes the stability of the complete closed-loop system and the convergence of the weights of the neural network. The proposed approach has been shown to be effective through simulation under the effect of the disturbances and the change of the desired trajectory.

Tracking the nitrogen leaching from different sources in bioretention systems with a process-based model

Bioretention systems are a typical stormwater management technology intended to reduce runoff and non-point source pollution. Multiple sources can contribute to nitrogen (N) leaching from bioretention systems, including the current rainfall-runoff (i.e., immediate leaching), recent rainfall-runoffs (i.e., fast leaching) and long-ago legacy N (i.e., slow leaching), however, there is a lack of methods to quantify these contributions. Here, a process-based Bioretention System (BRS) model for N leaching simulations was verified with artificially-labeled 15N data besides N data to reduce predictive uncertainties. The BRS model was then coupled with an Nitrogen Source Apportionment (NSA) module for N leaching tracking to quantify the contributions from different sources. Results from a bioretention system showed that additional 15N data reduced the predictive uncertainty of total N (TN) concentrations by 23 %. Immediate leaching, fast leaching and slow leaching increased, decreased and remained steady during the leaching process and accounted for 79.54 ± 5.08 %, 2.80 ± 1.74 % and 17.66 ± 4.11 % of N leaching, respectively. The dominant immediate leaching can be significantly weakened by a submerged zone, a shorter antecedent dry periods (ADP), a lower rainfall intensity or influent concentration. This study provides a framework to quantify N leaching from multiple sources with implications for long-term N leaching management.

Decadal Thermal Variability of the Upper Southern Ocean: Zonal Asymmetry

Abstract As the major sink of anthropogenic heat, the Southern Ocean has shown quasi-symmetric, deep-reaching warming since the mid-twentieth century. In comparison, the shorter-term heat storage pattern of the Southern Ocean is more complex and has notable impacts on regional climate and marine ecosystems. By analyzing observational datasets and climate model simulations, this study reveals that the Southern Ocean exhibits prominent decadal (>8 years) variability extending to ∼700-m depth and is characterized by out-of-phase changes in the Pacific and Atlantic–Indian Ocean sectors. Changes in the Pacific sector are larger in magnitude than those in the Atlantic–Indian Ocean sectors and dominate the total heat storage of the Southern Ocean on decadal time scales. Instead of heat uptake through surface heat fluxes, these asymmetric variations arise primarily from wind-driven heat redistribution. Pacemaker and preindustrial simulations of the Community Earth System Model version 1 (CESM1) suggest that these variations in Southern Ocean winds arise primarily from natural variability of the tropical Pacific, as represented by the interdecadal Pacific oscillation (IPO). Through atmospheric teleconnection, the positive phase of the IPO gives rise to higher-than-normal sea level pressure and anticyclonic wind anomalies in the 50°–70°S band of the Pacific sector. These winds lead to warming of 0–700 m by driving the convergence of warm water. The opposite processes, involving cyclonic winds and upper-layer divergence, occur in the Atlantic–Indian Ocean sector. These findings aid our understanding of the time-varying heat storage of the Southern Ocean and provide useful implications on initialized decadal climate prediction.

Resolution Dependence of Extreme Wind Speed Projections in the Great Lakes Region

Abstract The effect of anthropogenic climate change on extreme near-surface wind speeds is uncertain. Observed trends are weak and difficult to disentangle from internal variability, and model projections disagree on the sign and magnitude of trends. Standard coarse-resolution climate models represent the fine structures of relevant physical phenomena such as extratropical cyclones (ETCs), upper-level jet streaks, surface energy fluxes, and land surface variability less skillfully than their high-resolution counterparts. Here, we use simulations with the NCAR Community Earth System Model with both uniform (110 km) resolution and the variable-resolution configuration (VR-CESM-SONT, from 110 to 7 km) to study the effect of refined spatial resolution on projections of extreme strong and weak wind speeds in the Great Lakes region under end-of-century RCP8.5 forcing. The variable-resolution configuration projects strengthening of strong-wind events in the refined region with the opposite occurring in the uniform-resolution simulation. The two configurations provide consistent changes to synoptic-scale circulations associated with high-wind events. However, only the variable-resolution configuration projects weaker static stability, enhanced turbulent vertical mixing, and consequentially enhanced surface wind speeds because boundary layer dynamics are better captured in the refined region. Both models project increased frequency of extreme weak winds, though only VR-CESM-SONT resolves the cold-season inversions and summertime high temperatures associated with stagnant wind events. The identifiable mechanism of the changes to strong winds in VR-CESM-SONT provides confidence in its projections and demonstrates the value of enhanced spatial resolution for the study of extreme winds under climate change. Significance Statement In this study, we compare climate change projections of high and low extreme wind speeds in the Great Lakes region between a standard coarse-resolution simulation and a high-resolution simulation performed using the same climate model. The fine-resolution simulation projects strengthening high wind speeds, opposite to the coarse-resolution simulation. Both project increasing frequency of extreme weak winds, but the human-health-related impacts of stagnant winds are only captured at fine resolution. The changes in the coarse-resolution simulation are explained by changes to large-scale circulation, while the fine-resolution changes are linked to local processes the coarse model does not resolve. This helps explain the diverging projections of strong winds and gives greater credibility to the fine-resolution simulation.

Prototype Model of Electric Locomotive Traction System

Abstract: Electrical locomotive is driven by DC series motor of 12V. The torque produced by motors is used to move the locomotive. The main supply to the motor is given through pantographs. The total length of path is 6 feet approximately. The other main equipment are rectifiers, Step-down transformer. Rectifier is used for conversion of alternating current to direct current, Step-down transformers used for convert high voltage power to a low voltage The railway is one of the largest and oldest methods of the transportation in our country serving many people in many ways. The major problem in railways is derailing and obstruction to the locomotive in different ways. Traction is basically two types, electrical traction and nonelectric traction. Electric traction is most advantageous and economical with less maintenance when compared to other.

Monitoring of Water Quality using Machine Learning - A Review

Abstract: The incorporation of machine learning (ML) can improve the monitoring of water quality. It improves the sustainability and security of water resources by enabling forecasting and early detection of possible water contamination. A real-time system that collects information on numerous water parameters, including pH level, dissolved oxygen, turbidity, temperature, and conductivity, is made possible by the combination of IoT technology with ML algorithms. The system models complex links between water quality parameters and looks for variations from typical patterns using a mix of supervised and unsupervised learning and anomaly detection methods. Advantage of this method include cost-effectiveness, scalability, remote accessible, and real-time monitoring

Data-driven estimation of the algebraic Riccati equation for the discrete-time inverse linear quadratic regulator problem

In this paper, we propose a method for estimating the algebraic Riccati equation (ARE) with respect to an unknown discrete-time system from the system state and input observation. The inverse optimal control (IOC) problem asks, “What objective function is optimized by a given control system?” The inverse linear quadratic regulator (ILQR) problem is an IOC problem that assumes a linear system and quadratic objective function. The ILQR problem can be solved by solving a linear matrix inequality that contains the ARE. However, the system model is required to obtain the ARE, and it is often unknown in fields in which the IOC problem occurs, for example, biological system analysis. Our method directly estimates the ARE from the observation data without identifying the system. This feature enables us to economize the observation data using prior information about the objective function. We provide a data condition that is sufficient for our method to estimate the ARE. We conducted a numerical experiment to demonstrate that our method can estimate the ARE with less data than system identification if the prior information is sufficient.

Biomechanical gait analysis in sheep: kinematic parameters

Animals have been used as models to help to better understand biological and anatomical systems, and pathologies in both humans and non-human species, and sheep are often used as an in vivo experimental model for orthopedic research. Gait analysis has been shown to be an important tool in biomechanics research with clinical applications. The purpose of this study was to perform a kinematic analysis using a tridimensional (3D) reconstruction of the sheep hindlimb. Seven healthy sheep were evaluated for natural overground walking, and motion capture of the right hindlimb was collected with an optoelectronic system while the animals walked in a track. The analysis addressed gait spatiotemporal variables, hip, knee and ankle angle and intralimb joint angle coordination measures during the entire walking cycle. This study is the first that describes the spatiotemporal parameters from the hip, knee and ankle joints in a tridimensional way: flexion/extension; abduction/adduction and inter/external rotation. The results of this assessment can be used as an outcome indicator to guide treatment and the efficacy of different therapies for orthopedic and neurological conditions involving the locomotor system of the sheep animal model.

Modeling of solar CPVT system integrated with heat sinks in existence of hybrid nanofluid

Modeling of solar CPVT system integrated with heat sinks in existence of hybrid nanofluid

Optimal operation of flexible interconnected distribution grids based on improved virtual synchronous control techniques

With distributed energy sources connected to the distribution grid on a large scale for distributed photovoltaic power randomness, this paper proposes a flexible interconnection system optimization operation strategy. First, the virtual synchronous control technology is improved to improve the DC bus voltage stability; second, it analyzes the system operation mode to judge the output logic of PV and storage units, takes DC bus power balance as the underlying logic, and puts forward the power coordination optimization strategy and fault power supply restoration strategy with full consideration of factors such as the load balance degree of the distribution station area, the economic operation of the main transformer, and the amount of power lost in the faulty station area. It also establishes a multi-objective optimization model to obtain the power commands of each port and achieves the power flexibility mutualization of the flexible interconnected system through the accurate regulation of the soft normally open point (SNOP). Finally, a simulation model of the flexible interconnection system is built using MATLAB/Simulink to verify the effectiveness of the proposed optimization strategy.

Nonlinear Robust Control of Vehicle Stabilization System with Uncertainty Based on Neural Network

To effectively suppress the effects of uncertainties including unmodeled dynamics and external disturbances in the vehicle stabilization system, a nonlinear robust control strategy based on a multilayer neural network is proposed in this paper. First, the mechanical and electrical coupling dynamics model of the vehicle stabilization system, considering model uncertainty and actuator dynamics, is refined. Second, the lumped uncertainty of the vehicle stabilization system is estimated by a multi-layer neural network and compensated by feedforward control. The high robustness of the system is ensured by constructing the sliding mode feedback control law. The proposed control method overcomes the limitations of sliding mode technology and the neural network and is naturally applied to the vehicle stabilization system, avoiding the adverse effects of high-gain feedback. Based on Lyapunov theory, it is demonstrated that the proposed controller is able to achieve the desired stability tracking performance. Finally, the effectiveness of the proposed control strategy is verified by co-simulation and comparative experiments.

Nonlinear Dynamic Response of Galfenol Cantilever Energy Harvester Considering Geometric Nonlinear with a Nonlinear Energy Sink

The nonlinear energy sink (NES) and Galfenol material can achieve vibration suppression and energy harvesting of the structure, respectively. Compared with a linear structure, the geometric nonlinearity can affect the output performances of the cantilever beam structure. This investigation aims to present a coupled system consisting of a nonlinear energy sink (NES) and a cantilever Galfenol energy harvesting beam with geometric nonlinearity. Based on Hamilton’s principle, linear constitutive equations of magnetostrictive material, and Faraday’s law of electromagnetic induction, the theoretical dynamic model of the coupled system is proposed. Utilizing the Galliakin decomposition method and Runge–Kutta method, the harvested power of the external load resistance, and tip vibration displacements of the Galfenol energy harvesting model are analyzed. The influences of the external excitation, external resistance, and NES parameters on the output characteristic of the proposed coupling system have been investigated. Results reveal that introducing NES can reduce the cantilever beam’s vibration while considering the geometric nonlinearity of the cantilever beam can induce a nonlinear softening phenomenon for the output behaviors. Compared to the linear system without NES, the coupling model proposed in this work can achieve dual efficacy goals over a wide range of excitation frequencies when selecting appropriate parameters. In general, large excitation amplitude and NES stiffness, small external resistance, and small or large NES damping values can achieve the effect of broadband energy harvesting.

Offset-Free Koopman Model Predictive Control of Thermal Comfort Regulation for a Variable Refrigerant Flow-Dedicated Outdoor Air System-Combined System

Abstract Variable refrigerant flow (VRF) system has been an appealing solution of air conditioning for residential and commercial buildings, due to its flexibility and cost effectiveness, while lack of ventilation capability is a major drawback. Incorporation of dedicated outdoor air system (DOAS) is a typical practice. However, good coordination between DOAS and VRF is critical for achieving desired thermal comfort is challenging due to the possible complexity of mixed sensible and latent heat exchanges. In this paper, to handle the nonlinear dynamic characteristics of VRF-DOAS system, we propose an offset-free Koopman model predictive control (MPC) strategy for thermal comfort regulation, in which the MPC design is computationally more efficient due to the convex problem formulation and the use of reduced-order Koopman models, and the offset-free MPC structure enhances the robustness to model uncertainties and unmeasured disturbances. A control-oriented model is obtained by hybridizing the first-principle and data-driven modeling approach. The proposed controls strategy is evaluated with a Modelica simulation model of a VRF-DOAS system. A Dymola-Python cosimulation platform is developed via the functional mockup interface (FMI), for which the MPC algorithms are implemented in Python. Simulation results show significantly better performance of the offset-free Koopman MPC in thermal comfort regulation.

A novel coordinate transformation stability criterion and parameter selection for grid-connected inverter

The negative resistance of grid-connected inverter (GCI) and the increasing number of GCI in power grid pose great challenges to the stability of GCI. This paper proposes a coordinate transformation stability criterion (CTSC) based on the single-input single-output (SISO) impedance model for GCI system. A low complexity fractional polynomial model of GCI is derived by the Pade approximation and the SISO impedance model in the αβ domain. CTSC is proposed by applying the Nyquist stability criterion, which simplifies the graph trajectory criterion to a numerical criterion. Since the effect of parameter on stability is usually limited to the neighborhood, average parameter assessment metric (APAM) is proposed based on CTSC to evaluate the impact of each parameter over a wide range. Additionally, joint parameter assessment metric (JPAM) is proposed to simultaneously adjust multiple parameters on stability. Finally, compared with the three criteria for conservatism analysis, CTSC possesses the lowest conservatism. The theory proposed in this paper is verified by experiment.