Changes In Loading Conditions Research Articles

HULL REDESIGN AND ITS EFFECT ON THE RESISTANCE OF MANADO PROTOTYPE SMALL PURSE SEINER

This research aims to determine the effect of redesigning the hull of a small purse seine ship on its motion resistance. The research was carried out using a small purse seine ship prototype in Manado by changing the length-breath-depth ratio based on the main dimensions of the ship, resulting in three new hull designs coded K-0 (prototype), K-1, K-2, and K- 3. Maxsurf modeler and Maxsurf resistance were used to run a simulation set of three loading conditions (light, half, full) and speed (low, medium, high). The research results show that changes in ship hull design affect the resistance and thrust of the ship. There is a difference in resistance between the ship with the redesigned hull and the prototype, where the smallest difference is shown by K-3. In addition to changes in hull design, changes in ship loading conditions, Froude number, and ship speed lead to increases in ship resistance and thrust. Based on the average allowance (sea margin or service margin) in shipping lanes, the need for propulsion power and the number of propulsion power vessels K-0 (prototype) and K-3 are still better than K-1 and K-2. Keywords: purse-seiner, resistance, speed, propulsion

Rapid prediction of grain boundary network evolution in nanomaterials utilizing a generative machine learning approach

Predicting the behavior of nanomaterials under various conditions presents a significant challenge due to their complex microstructures. While high-fidelity modeling techniques, such as molecular dynamics (MD) simulations, are effective, they are also computationally demanding. Machine learning (ML) models have opened new avenues for the rapid exploration of design spaces. In this work, we developed a deep learning framework based on a conditional generative adversarial network (cGAN) to predict the evolution of grain boundary (GB) networks in nanocrystalline materials under mechanical loads, incorporating both morphological and atomic details. We conducted MD simulations on nanocrystalline tungsten and used the resulting ground-truth data to train our cGAN model. We assessed the performance of our cGAN model by comparing it to a Convolutional Autoencoder (ConvAE) model and examining the impact of changes in geometric morphology and loading conditions on the model's performance. Our cGAN model demonstrated high accuracy in predicting GB network evolution under a variety of conditions. This developed framework shows potential for predicting various materials' behaviors across a wide range of nanomaterials.

Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques

Microgrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimizing MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control systems are extensively used due to their interpretability and simplicity. However, these strategies frequently lack the flexibility for complex and changing system dynamics. This paper provides a novel method called hybrid intelligent control for adaptive MG that integrates basic rule-based control and deep learning techniques, including gated recurrent units (GRUs), basic recurrent neural networks (RNNs), and long short-term memory (LSTM). The main target of this hybrid approach is to improve MG management performance by combining the strengths of basic rule-based systems and deep learning techniques. These deep learning techniques readily enhance and adapt control decisions based on historical data and domain-specific rules, leading to increasing system efficiency, stability, and resilience in adaptive MG. Our results show that the proposed method optimizes MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyperparameter optimization techniques with the aim of predicting power consumption and generation within the adaptive MG system. Our promising results show the highest-performing models indicating high accuracy and efficiency in power prediction. The finest-performing model accomplishes an R2 value close to 1, representing a strong correlation between predicted and actual power values. Specifically, the best model achieved an R2 value of 0.999809, an MSE of 0.000002, and an MAE of 0.000831.

Oxygen starvation control of proton exchange membrane fuel cell through fusion control strategy

Oxygen excess ratio (OER) is regarded as a significant indicator of the safety and efficient operation of the open-cathode proton exchange membrane fuel cell (PEMFC) system. This is because inappropriate OER is prone to the phenomenon of oxygen starvation and oxygen saturation under load conditions change frequently. To avoid the phenomenon, this paper integrates linear active disturbance rejection control (LADRC) and proportional integral differential (PID) control to regulate OER of fuel cell air supply system, which effectively combines model-based and data-driven LADRC structure. As the controller parameters directly affect the control performance and it is difficult to obtain the optimal control parameters. Especially, when the load conditions change, the control parameters are difficult to adapt the load changes. To this end, a snake optimization (SO) algorithm with easy implementation and fast convergence is used to realtime update the controller parameters, which can further improve the control effects. Firstly, an equivalent linear model of fuel cell air supply system is established. Secondly, a hybrid LADRC method is developed by combining the merits of the LADRC and PID methods. Thirdly, the SO algorithm is used to optimize the controller parameters, which can achieve the optimal control parameters. Finally, the results revealed that the proposed method has a faster response with less noise sensitivity against the load changes and stronger robustness than the other methods.

Biventricular function in preterm infants with patent ductus arteriosus ligation: A three-dimensional echocardiographic study.

The detailed hemodynamics after patent ductus arteriosus (PDA) ligation in preterm infants remain unknown. We aimed to clarify the effect of surgical ligation on left ventricular (LV) and right ventricular (RV) volume and function. Echocardiography was performed in 41 preterm infants (median gestational age: 25 weeks) before and after PDA ligation. Global longitudinal strain was determined using three-dimensional speckle-tracking echocardiography. These values were compared with those in 36 preterm infants without PDA (non-PDA). Preoperatively, the PDA group had greater end-diastolic volume (EDV) and cardiac output (CO) in both ventricles, a higher LV ejection fraction (LVEF) (53% vs 44%) and LV global longitudinal strain, and a lower RVEF (47% vs 52%) than the non-PDA group. At 4-8 h postoperatively, the two groups had a similar LVEDV and RVEDV. However, the PDA group had a lower EF and CO in both ventricles than the non-PDA group. At 24-48 h postoperatively, the RVEF was increased, but the LVEF remained decreased, and LVCO was increased. PDA induces biventricular loading and functional abnormalities in preterm infants, and they dramatically change after surgery. Three-dimensional echocardiography may be beneficial to understand the status of both ventricles. Preterm infants are at high risk of hemodynamic compromise following a sudden change in loading conditions after PDA ligation. Three-dimensional echocardiography enables quantitative and serial evaluation of ventricular function and volume in preterm infants with PDA. PDA induces biventricular loading and functional abnormalities in preterm infants, and they dramatically change after surgery.

Photosvaltable Characteristics Monitoring System Using the Internet of Things (IOT)


 
 
 
 Continuous monitoring of solar panel performance is essential to ensure effective and efficient generation of electrical energy. Developing a monitoring system such as an I-V characteristic curve tracker enables real-time monitoring and identification of potential problems or efficiency improvements. The system utilizes a circuit switching system for accurate and continuous measurement, providing insight into how the solar panel responds to changing environmental and load conditions. The data recorded by temperature, light intensity, current, and voltage sensors on the solar panel provides valuable information about its condition and the environmental factors affecting it. Utilizing IoT technology allows real-time monitoring of the data, accessible via a web page and presented graphically. The data is also stored in Excel format for further analysis, so users can access and monitor continuously as long as they are connected to the internet. From the experimental results, the Voc of the solar panel ranged from 18.7 V to 20.42 V, and Isc between 0.0727 A to 0.2145 A, which were tested under solar irradiation ranging from 100 to 500 W/m². This research demonstrates the importance of continuous monitoring and the effectiveness of the developed monitoring system in providing real-time data for analysis and optimization of solar panel performance. The system has been tested to meet the IEC No. 13B-23 standard for accuracy classes 0.2 and 0.5. A comparison between manual testing and sensor testing shows that despite the difference in accuracy levels, the sensor measurements have a relatively low average error (1.04% for voltage and 0.64% for current) compared to manual measurements, indicating that the sensor is reliable in providing accurate data. This demonstrates the reliability and effectiveness of using the sensor in the solar panel characteristics monitoring system.
 
 
 

Satellite-Based Background Aerosol Optical Depth Determination via Global Statistical Analysis of Multiple Lognormal Distribution

Determining background aerosol optical depth threshold value (BAOD) is critical to aerosol type identification and air pollution control. This study presents a statistical method to select the best BAOD threshold value using the VIIRS DB AOD products at 1 × 1 degree resolution from 2012 to 2019 as a major testbed. A series of multiple lognormal distributions with 1 to 5 peaks are firstly applied to fit the AOD histogram at each grid point, and the distribution with the highest correlation coefficient (R) gives preliminary estimations of BAOD, which is defined as either the intersection point of the first two normal distribution curves when having multiple peaks, or the midpoint between the peak AOD and the first AOD with non-zero probability when the mono peak is the best fit. Then, the lowest 1st to 100th percentile AOD distributions are compared with the preliminary BAOD distribution on a global scale. The final BAOD is obtained from the best cutoff percentile AOD distributions with the lowest bias compared with preliminary BAOD. Results show that the lowest 30th percentile AOD is the best estimation of BAOD for different AOD datasets and different seasons. Analysis of aerosol chemical information from MERRA-2 further supports this selection. Based on the BAOD, we updated the VIIRS aerosol type classification scheme, and the results show that the updated scheme is able to achieve reliable detection of aerosol type change in low aerosol loading conditions.

Experimental Structural Validation of a Landing Gear System for High-Speed Helicopter Applications

The design and technological demonstration of a Landing Gear architecture were addressed for Airbus fast rotorcraft end application within the Clean Sky 2 Racer project. Numerical activities including advanced modelling approaches were carried out to substantiate the feasibility of structural concepts in compliance with industrial standards and CS-29 applicable airworthiness requirements. In order to demonstrate the goodness of design strategies, a true-scale prototype was manufactured and tested for demonstrating its capability to withstand static loads representative of the limit and ultimate cases expected in service. The paper will focus on the qualification of the Nose and Main Landing Gear systems. Sizing process was validated and verified by test whose results allowed for validating/calibrating the FE model. In such a way, the design database could count on a reliable tool available for analysing the effect of any further load condition change.

An Adaptive Method for Multifault Diagnosis of Induction Motor Under Sharp Changing Speed and Load Condition

An Adaptive Method for Multifault Diagnosis of Induction Motor Under Sharp Changing Speed and Load Condition

A robust damping control for battery energy storage integrated power systems to mitigate inter-area oscillations

This paper presents the effect of a Battery Energy Storage System (BESS) on the power system inter-area oscillations under changing load conditions. The dynamic interaction between the BESS control modes and synchronous generators, along with BESS control parameters, influence the damping of prominent inter-area oscillations under different loading conditions. Furthermore, H∞ mixed sensitivity scheme-based Wide-Area Damping Controller (WADC) is proposed for BESS. This controller is designed using the Linear Matrix Inequality (LMI) framework to mitigate the prominent inter-area oscillation of the power system. Sensitivity to control parameter variation and the geometric measure of the observability are utilized to select the location and feedback signal for the WADC. The performance of the proposed WADC is compared with an Energy Storage System-based stabilizer and WADC proposed in an existing work on a modified New England ten-machine benchmark system. The Real-Time Digital Simulator (RTDS) platform is used to assess the real-time viability of the proposed WADC. The simulation results demonstrate that the proposed WADC can effectively diminish several inter-area modes. Additionally, it offers robust performance under varying load conditions, contingencies and with communication delays in feedback signals. The proposed controller is also effective under the system integrated with Doubly Fed Induction Generator-based Wind Turbine System.

Mechanical Behavior Monitoring and Load Inversion Analysis of Large-Diameter Underwater Shield Tunnel during Construction.

The construction of large-diameter shield tunnels underwater involves complex variations in water and earth load outside the tunnel segment, as well as intricate mechanical responses. This study analyzes the variation laws of external loads, axial forces, and bending moments acting on the segment ring during the shield assembly and removal from the shield tail. It accomplishes this through the establishment of an on-site monitoring system based on the Internet of Things (IoT) and proposes a Bayesian-genetic algorithm model to estimate the water and earth pressure. The fluctuation section exhibits a peak load twice as high as that in the stable section. These variations are influenced by Jack thrust, shield shell force, and grouting pressure. The peak load observed in the fluctuation section is twice as high as the load observed in the stable section. During the shield tail removal process, the internal forces undergo significant fluctuations due to changes in both load and boundary conditions, and the peak value of the axial force during the fluctuation section is eight times higher than that during the stable section, while the peak value of the bending moment during the fluctuation section is five times higher than that during the stable section. The earth and water pressure calculated using the inversion analysis method, which relies on the measured internal forces, closely matches the actual measured values. The results demonstrate that the accuracy of the water and earth pressure obtained through inversion analysis is twice as high as that obtained using the full coverage pressure method. These results can serve as a valuable reference for similar projects.

A viscoplastic constitutive model for plastic silts and clays for static slope stability applications

A viscoplastic model for representing plastic silts and clays in geotechnical static slope stability applications is presented. The PM4SiltR model builds on the stress ratio-controlled, critical state-based, bounding surface plasticity model PM4Silt and is coded as a dynamic link library for use in the finite difference program FLAC 8.1. PM4SiltR incorporates strain rate-dependent shear strength, stress relaxation, and creep using a consistency approach combined with an internal strain rate and auto-decay process. The model does not include a cap, and as such cannot simulate strain rate-dependent consolidation under increasing overburden stress. Six parameters control the viscous response for PM4SiltR while the parameters controlling the nonviscous components of the response are the same as for PM4Silt. Single element simulations are presented to illustrate the influence of viscoplasticity on the constitutive response in direct simple shear loading and undrained creep. Single element responses are shown to be consistent with observed experimental results. Simulations of a hypothetical tailings dam constructed using the upstream method are performed to illustrate use of PM4SiltR at field scale. Results of field scale simulations show PM4SiltR can model undrained creep and progressive failure leading to delayed slope instability after relatively minor changes in loading conditions at field scale.

Dense-YOLOv7: improved real-time insulator detection framework based on YOLOv7

Abstract Due to various mechanical and electrical stresses caused by changes in environmental and power load conditions, insulators may fail, resulting in serious economic losses. Manual cleaning of insulators is subject to weather and environmental constraints and poses significant safety risks. Traditional insulator detection methods have problems such as slow detection speed and poor robustness. Therefore, this paper proposes a real-time insulator detection algorithm based on the improved YOLOv7. First, in order to effectively streamline the number of parameters, Dense-YOLOv7 adopts the dense connection concept of DenseNet to design DenseBlock. Second, replacing the loss function with Focal Loss to solve the problem of unbalanced matching of foreground and background sample quantities has improved the detection accuracy of the model. Finally, to address the issue of PReLU and LeakyReLU activation functions being insensitive to spatial information, the activation function is replaced with FReLU to improve the robustness of the model. The experimental dataset used in this paper is a combination of the Chinese Power Transmission Line Insulator Dataset (CPTLID) and our own dataset. The experimental results show that the improved algorithm in this paper has only 44.23 M parameters and a detection speed of 44.87 FPS, which is 4.8% less than that of YOLOv7 and 8.14% quicker than that of YOLOv7. Experimental results show that Dense-YOLOv7 can significantly streamline the model parameter size while maintaining high accuracy and effectively improve the detection speed, which can meet the application requirements of real-time insulator detection.

Design Strategy and Implementation of Control Parameters for BLDC Motor Application in an Electric Vehicle

The brushless dc motor (BLDC) motor is an electric motor that is widely utilized in numerous applications, especially in automotive systems. It is significant to maximize the force of the electric vehicle; however, due to the rapid growth of electric vehicles, motors and power electronics are determined with huge concern. Meanwhile, the Proportional Integral Derivative (PID) cannot balance the load in a stable condition, and this variation generates a change in load and track conditions. In mobile robot the BLDC motor diminished the current and it increased the torque voltage and current by 48V and 50A, respectively. So to overcome these issues, the control algorithm is implemented in this paper to regulate the speed of the motor in 1KW BLDC in an Electric Vehicle (EV). A 1500rpm BLDC motor is designed by Dymola software which is evaluated with MATLAB. The BLDC increases the torque speed characteristics of the motor based on the pole configuration of an electric vehicle. Evaluation of the method is determined with a set of STM 31 ARM processors and a 750 watt driver. The experimentation is simulated using Arduino IDE software and the results revealed that it is applicable for STM 32 or teensy 3.2 development board or Arduino Mega development board.

Analysis and Simulation of Three-Input DC-DC Converter with Bipolar Symmetric Outputs

This paper presents a three-input high-gain DC-DC converter with bipolar symmetric outputs. The advantages of a bipolar DC grid are higher reliability, flexibility, and efficiency as compared with the unipolar DC grid. Therefore, the proposed converter is designed to connect directly with the bipolar DC grid due to its bipolar symmetric outputs. The three input voltage sources to the converter are operated in a sequential manner from the first voltage source to the last one, with each source providing power at a level proportional to its duty cycle. Similarly, when the sources are equal, they operate in a sequential manner with their specific power. In addition, the proposed converter is designed to provide the required power to the grid under both simultaneous or individual from three input voltage sources. A double-loop PI controller is applied to regulate the converter output voltages and the control loop is analyzed and the transfer function relating reference voltage to output voltage is derived. Voltage balance control is provided to ensure that the outputs remain symmetrical despite changes in load conditions or input voltage. A converter with a power rating of 1000W and output voltage levels of 200V, -200V, and 400V is designed. The three supply voltage of 150V,100V, and 75V are used. The converter is tested under a variation of supply voltages and load using MATLAB/SIMULINK. The results show the feasibility and effectiveness of the proposed converter.

A digital twin-based framework for damage detection of a floating wind turbine structure under various loading conditions based on deep learning approach

Engineering has many necessary fields, and Structural Health Monitoring (SHM) is one of the most important of them. Sometimes in industrial environments, it is difficult and even impossible to collect data containing different real damages. Therefore, the problem of data acquisition represents a primary challenge in designing damage detection systems. The application of digital twin methods based on simulated models and/or Machine Learning (ML) models is a practical way to solve this problem. In this approach, a digital twin is generated for a compromised structure, utilizing a physics-based model to analyze diverse damage scenarios. Subsequently, an ML model is trained using data extracted from the physics-based model, functioning as the digital twin. This research proposes a method based on a digital twin for detecting damages in structures. The data produced from a Floating Wind Turbine (FWT) model was used to evaluate the performance of the proposed digital twin-based method. For this purpose, the FWT structure was simulated using a numerical model to address the data collection problem in the face of various uncertainties, such as changing loading conditions. In line with the concept of digital twin and to reduce the computational time, a Deep Convolution Long Short-Term Memory Neural Network (DCLSTMNN) model was designed and trained only with the frequency data of various scenarios of the simulated FWT model under constant loads (deterministic loads, including constant wind speed and airy wave model) to learn the damage-sensitive features. Then, to demonstrate the robustness of the proposed model under different uncertainties, the DCLSTMNN model was evaluated using the frequency data of the simulated FWT model under variable loading conditions (including Kaimal wind model and JONSWAP wave theory). Some vibration response components unrelated to the nature of the FWT model were removed using the Complete Ensemble Empirical Mode Decomposition (CEEMD) method. Then, the reconstructed vibration responses were used to create the frequency data using the Frequency Domain Decomposition (FDD) technique. The study results show that the proposed digital twin-based method can detect the location and severity of damage more accurately than other comparable methods despite various uncertainties.

Dynamically reprogrammable stiffness in gecko-inspired laminated structures

Adaptive structures are of interest for their ability to dynamically modify mechanical properties post fabrication, enabling structural performance that is responsive to environmental uncertainty and changing loading conditions. Dynamic control of stiffness is of particular importance as a fundamental structural property, impacting both static and dynamic structural performance. However, existing technologies necessitate continuous power to maintain multiple stiffness states or couple stiffness modulation to a large geometric reconfiguration. In this work, reversible lamination of stiff materials using gecko-inspired dry adhesives is leveraged for bending stiffness control. All stiffness states are passively maintained, with electrostatic or magnetic actuation applied for ∼1 s to reprogram stiffness. We demonstrate hinges with up to four passively maintained reprogrammable states decoupled from any shape reconfiguration. Design guidelines are developed for maximizing stiffness modulation. Experimentally, the proposed method achieved a stiffness modulation ratio of up to 14.4, with simulations showing stiffness modulation ratios of at least 73.0. It is anticipated that the stiffness reprogramming method developed in this work will reduce energy requirements and design complexity for adaptation in aerospace and robotics applications.

Performance analysis of FEC based SR motor drive using fuzzy tuned PI controller

Purpose In this paper, a new front end converter (FEC) topology has been proposed for the switched reluctance (SR) motor drive. This study aims to present the performance analysis of FEC-based SR motor drive using various types of control schemes like conventional proportional integral (PI) controller, fuzzy logic controller (FLC) and fuzzy-tuned proportional integral controller (Fuzzy-PI). Design/methodology/approach The proposed FEC-based SR motor drive with various control strategies is derived for the torque ripple minimization and speed control. Findings The steady state and the dynamic response of the FEC-based SR motor drive are analyzed using three different controllers under change in speed and loading conditions. The Fuzzy-PI-based control scheme improves the dynamic response of the system when compared with the FLC and the conventional PI controller. Originality/value The hardware prototype has been implemented for the FEC-based SR motor drive by using the Xilinx SPARTAN 6 FPGA processor. The experimental verification has been conducted and the results have been measured under steady state and dynamic conditions.

Evaluation of Intraoperative Left-Ventricular Diastolic Function by Myocardial Strain in On-Pump Coronary Artery Bypass Surgery

Left ventricular (LV) diastolic function strongly predicts outcomes after cardiac surgery, but there is no consensus about appropriate intraoperative assessment. Recently, intraoperative diastolic strain-based measurements assessed by transesophageal echocardiography (TEE) have shown a strong correlation with LV relaxation, compliance, and filling, but there are no reports about evaluation through the entire perioperative period. Therefore, the authors describe the intraoperative course of this novel assessment technique in patients who underwent coronary artery bypass grafting, and compare it with conventional echocardiographic measures and common grading algorithms of LV diastolic dysfunction (LVDD). Prospectively obtained data. A single university hospital. Thirty adult patients scheduled for isolated on-pump coronary artery bypass grafting surgery with preoperative preserved left and right ventricular systolic function, without significant heart valve disease and pulmonary hypertension, and an uneventful intraoperative course were included. Transesophageal echocardiography was performed after induction of anesthesia (T1), after termination of cardiopulmonary bypass (T2), and after sternal closure (T3). Echocardiographic evaluation was performed in stable hemodynamic conditions, in sinus rhythm or atrial pacing, and vasopressor support with norepinephrine ≤0.1 µg/kg/min. Strain-based measurements of peak longitudinal strain rate during isovolumetric relaxation (SR-IVR) and during early (SR-E) and late (SR-A) LV filling were assessed using EchoPAC v204 software (GE Vingmed Ultrasound AS, Norway). Evaluation of conventional echocardiographic parameters included transmitral Doppler measures of early (E) and late (A) LV filling, as well as lateral-tissue Doppler velocity assessed during early (e´) and late (a´) LV filling, tricuspid regurgitation, and left atrial dilatation. Evaluation and grading of LV diastolic function by myocardial strain was feasible in all included patients at all time points of assessment. Using conventional grading algorithms, however, a substantial number of patients could not be sufficiently graded, falling into an indeterminate zone and not reliably estimating LVDD (T1, 40%; T2, 33%; T3, 36%). There was significant impairment of LV diastolic function after bypass, as measured by SR-IVR (T1 v T2, 0.28 s-1 [IQR 0.23; 0.31) v 0.18 s-1 [IQR 0.14; 0.22]; p < 0.001), SR-E (T1 v T2, 0.95 ± 0.34 s-1v 1.28 ± 0.36 s-1; p < 0.001), and E/SR-IVR (T1 v T2, 2.3 ± 1.0 m v 4.5 ± 2.1 m; p < 0.001]. Conventional echocardiographic measures remained unchanged during the same period (E/A T1 v T2, 1.27 [IQR 0.94; 1.59] v 1.21 [IQR 1.03; 1.47] [p = 1] and E/e´ T1 v T2, 7.0 [IQR 5.3; 9.6] v 6.35 [IQR 5.7; 9.9] [p = 0.9]). There were no significant changes in the values of SR-IVR, SR-E, SR-A, E/SR-IVR, E/A, and E/e´ before and after sternal closure (T2 v T3). Intraoperative assessment of strain-based measurements of LV diastolic function and strain-based LVDD grading was feasible in this group of selected patients, whereas conventional parameters failed to describe LVDD sufficiently in a substantial number of patients. Diastolic strain-based measurements showed impairment of LV relaxation and compliance after bypass, which was not detected by conventional echocardiographic parameters. Therefore, diastolic myocardial strain analysis might be more sensitive in detecting myocardial diastolic dysfunction by TEE in the perioperative setting, with its dynamic changes of loading conditions, and might provide valuable and additional information on the perioperative changes of LV diastolic function.

CFD investigation into the effect of GM variations on ship manoeuvring characteristics

The change in a ship's loading condition may induce a displacement of its vertical centre of gravity, subsequently impacting the metacentric height (GM) of the vessel. Such alterations can have unforeseen effects on the ship's manoeuvring behaviour. This study investigates these effects by developing a free running manoeuvre model for the KRISO Containership (KCS) based on the Unsteady Reynolds-Averaged Navier-Stokes (URANS) method. A multi-level dynamic overset grid and the body-force propeller method were employed to simulate the ship motion involving rudder manipulations. The numerical calculations of zigzag and turning manoeuvre revealed that the variations in GM produce significant alternations in the roll motion during manoeuvres, consequently affecting the zigzag and turning trajectories of the vessel.