Simulation Tool Research Articles

Experimental parameter identification of scalable white and grey box jounce bumper models for ride comfort simulation

The design of a passenger car’s ride and handling goals requires an enormous amount of hardware and prototypes. To avoid time-consuming testing activities, one crucial trend is the virtualisation of suspension development. Another goal of the virtualisation is the determination of vehicle properties at an early stage of the development process. Highly accurate suspension models are required to transfer the full vehicle’s characteristics into simulation tools. Since the jounce bumper is an important component of the suspension system, modelling its functional behaviour is essential for virtualised chassis development. Therefore, two dynamic modelling approaches are derived and compared in this contribution. One approach is based on local linear model networks and thus can be seen as a grey box model. The other approach consists of white box rheological elements, which allow the physical interpretability of the model parameters. The comparison of the approaches is based on experimental parameter identification, which includes synthetic and real excitations recorded on representative roads. The advantages of the proposed approaches will be discussed in terms of their suitability for the virtual design of the driving behaviour.

Dynamic load reference based DQ-axis synchronous frame control method of grid connected PV hybrid microgrid

The hybrid AC-DC microgrid is a system that links AC and DC microgrids using a bidirectional AC-DC interface converter. The presence of dynamic loads is a major obstacle to the implementation of the microgrid concept, and those 50 % include asynchronous motors. This paper presents a control system for a hybrid microgrid that powers an electrical motor using the field-oriented control (FOC) method. This study investigates the significance of third-harmonic injected PWM (THIPWM) in reducing the necessary voltage across the DC bus while still achieving the required AC voltage at motor terminals. MATLAB/Simulink simulation tool and OPAL-RT for real-time simulations were used for validation of the proposed control. The outcomes show the tracking of the reference currents, achieving accurate torque control, managing various operating modes like grid-connected having SPWM and THIPWM, or operation with PV and battery sources. The research also highlights lower settling time values as well as lower total harmonic distortion (THD) compared to SPWM in different load conditions using THIPWM.

The application and algorithm of fabric simulation in game industry

fabric simulation has emerged as a crucial aspect of computer graphics, particularly in applications such as film production, video games, and design. This paper provides a comprehensive review of fabric simulation methodologies, implementation techniques, and its diverse applications within the game industry. The fabric simulation algorithms are broken down into Fabric Modeling, Dynamic Simulation, and Collision Handling. The three common modeling strategies of Elasticity-Based Models, Particle-Based Models, and Mass-Spring Damper Models, are discussed in detail. Additionally, dynamic simulation strategies such as Position-Based Dynamics (PBD) and Extended Position-Based Dynamics (XPBD) are explored for their role in real-time simulations, particularly in the game industry. The paper also delves into collision handling algorithms, exploring the strategies for accurate collision detection and response. The application of fabric simulation in the game industry is highlighted, showcasing how it enhances realism and immersion by simulating fabric movement and interactions with the environment. The paper introduced various fabric simulation tools and systems within popular game engines like Unity 3D and Unreal Engine 5. Furthermore, optimization strategies, such as view-dependent adaptive simulation, are discussed to improve performance providing insight for future work.

A novel design of multiplexer based on the cellular interaction in quantum dot technology with energy dissipation analysis

This paper addresses the challenge of efficiently designing multiplexer structures in quantum-dot technology circuits. Specifically, it introduces a novel approach to constructing a 2-to-1 multiplexer through a unique gate design that relies on the interaction between cells. The key focus of this design is to minimize the utilization of cells, thereby conserving space and facilitating the creation of more complex combinational and sequential circuits. The methodology involves developing a gate structure that requires the least number of cells compared to existing designs. This reduction in cell usage not only optimizes space utilization but also enhances the scalability of the multiplexer for integration into larger circuits. The proposed gate structure demonstrates a significant improvement, achieving a 15% reduction in the number of cells compared to the previously best-known design. To further evaluate the effectiveness of this novel gate design, the study extends its analysis to include the implementation of various complex circuits such as a 4-to-1 multiplexer, D latch, and T latch. These structures are synthesized using the newly proposed gate design as a fundamental building block. To validate the functionality and performance of these circuits, simulations are conducted using the QCADesigner simulation tool. This comprehensive simulation enables a thorough assessment of the proposed structures across different circuit configurations, providing insights into their operational efficiency and suitability for practical applications in quantum-dot technology.

AscentAM: A Software Tool for the Thermo-Mechanical Process Simulation of Form Deviations and Residual Stresses in Powder Bed Fusion of Metals Using a Laser Beam

Due to the tool-less fabrication of parts and the high degree of geometric design freedom, additive manufacturing is experiencing increasing relevance for various industrial applications. In particular, the powder bed fusion of metals using a laser beam (PBF-LB/M) process allows for the metal-based manufacturing of complex parts with high mechanical properties. However, residual stresses form during PBF-LB/M due to high thermal gradients and a non-uniform cooling. These lead to a distortion of the parts, which reduces the dimensional accuracy and increases the amount of post-processing necessary to meet the defined requirements. To predict the resulting residual stress state and distortion prior to the actual PBF-LB/M process, this paper presents the finite-element-based simulation tool AscentAM with its core module and several sub-modules. The tool is based on open-source programs and utilizes a sequentially coupled thermo-mechanical simulation, in which the significant influences of the manufacturing process are considered by their physical relations. The simulation entirely emulates the PBF-LB/M process chain including the heat treatment. In addition, algorithms for the part pre-deformation and the export of a machine-specific file format were implemented. The simulation results were verified, and an experimental validation was performed for two benchmark geometries with regard to their distortion. The application of the optimization sub-module significantly minimized the form deviation from the nominal geometry. A high level of accuracy was observed for the prediction of the distortion at different manufacturing states. The process simulation provides an important contribution to the first-time-right manufacturing of parts fabricated by the PBF-LB/M process.

Hybrid Quantum-Dot Cellular Automata Nanocomputing Circuits

Quantum-dot cellular automata (QCA) is an emerging transistor-less field-coupled nanocomputing (FCN) approach to ultra-scale ‘nanochip’ integration. In QCA, to represent digital circuitry, electrostatic repulsion between electrons and the mechanism of electron tunnelling in quantum dots are used. QCA technology can surpass conventional complementary metal oxide semiconductor (CMOS) technology in terms of clock speed, reduced occupied chip area, and energy efficiency. To develop QCA circuits, irreversible majority gates are typically used as the primary components. Recently, some studies have introduced reversible design techniques, using reversible majority gates as the main building block, to develop ultra-energy-efficient QCA circuits. However, this approach resulted in time delays, an increase in the number of QCA cells used, and an increase in the chip area occupied. This work introduces a novel hybrid design strategy employing irreversible, reversible, and partially reversible QCA gates to establish an optimal balance between power consumption, delay time, and occupied area. This hybrid technique allows the designer to have more control over the circuit characteristics to meet different system needs. A combination of reversible, irreversible, and innovative partially reversible majority gates is used in the proposed hybrid design method. We evaluated the hybrid design method by examining the half-adder circuit as a case study. We developed four hybrid QCA half-adder circuits, each of which simultaneously incorporates various types of majority gates. The QCADesigner-E 2.2 simulation tool was used to simulate the performance and energy efficiency of the half-adders. This tool provides numerical results for the circuit input/output response and heat dissipation at the physical level within a microscopic quantum mechanical model.

Mathematical Tools for Simulation of 3D Bioprinting Processes on High-Performance Computing Resources: The State of the Art

Three-dimensional (3D) bioprinting belongs to the wide family of additive manufacturing techniques and employs cell-laden biomaterials. In particular, these materials, named “bioink”, are based on cytocompatible hydrogel compositions. To be printable, a bioink must have certain characteristics before, during, and after the printing process. These characteristics include achievable structural resolution, shape fidelity, and cell survival. In previous centuries, scientists have created mathematical models to understand how physical systems function. Only recently, with the quick progress of computational capabilities, high-fidelity and high-efficiency “computational simulation” tools have been developed based on such models and used as a proxy for real-world learning. Computational science, or “in silico” experimentation, is the term for this novel strategy that supplements pure theory and experiment. Moreover, a certain level of complexity characterizes the architecture of contemporary powerful computational resources, known as high-performance computing (HPC) resources, also due to the great heterogeneity of its structure. Lately, scientists and engineers have begun to develop and use computational models more extensively to also better understand the bioprinting process, rather than solely relying on experimental research, due to the large number of possible combinations of geometrical parameters and material properties, as well as the abundance of available bioprinting methods. This requires a new effort in designing and implementing computational tools capable of efficiently and effectively exploiting the potential of new HPC computing systems available in the Exascale Era. The final goal of this work is to offer an overview of the models, methods, and techniques that can be used for “in silico” experimentation of the physicochemical processes underlying the process of 3D bioprinting of cell-laden materials thanks to the use of up-to-date HPC resources.

Compact Modified Elliptical-Shaped Ultra-wideband Filtering Antenna with Improved Band-Edge Selectivity

A compact filtering antenna with higher selectivity near band-edge frequencies for ultra-wideband (UWB) applications is presented and developed in this article. The antenna with filtering characteristics is formulated by integrating a rectangular-impedance resonator-based bandpass filter into the feedline of a modified elliptical-shaped monopole radiator. Initially, a modified elliptical-shaped monopole radiator is designed and numerically simulated to ensure the overall performance over the UWB band. The modified elliptical shape is used as a radiator to increase the electrical length while maintaining minimum physical size. The size of the UWB antenna is merely 0.518λ AL × 0.272 λ AL (where λAL is the guided wavelength at 2.62 GHz). Furthermore, to enhance the performance characteristics of the filtering antenna along with band-edge selectivity, geometrical parameters are optimized using a numerical simulation tool. It exhibits a simulated impedance bandwidth of 112% (3.05–10.81 GHz) with an electrical size of 0.676λ FL × 0.316λ FL (where λFL is the guided wavelength at 3.05 GHz). The experimental results of the fabricated UWB antenna and filtering antenna prototypes agree well with their simulated results.

ENTIMOS: Decision Support Tool Highlights Potential Impact of Non-intravenous Therapies for Multiple Sclerosis on Patient Care via Clinical Scenario Simulation.

Administration of intravenous (IV), high-efficacy treatments (HETs) for the treatment of multiple sclerosis (MS) poses a high resourcing and planning burden on infusion centres, resulting in treatment delays that may increase the risk of breakthrough disease activity. Simulation tools can be used to systematically analyse capacity scenarios and identify and better understand constraints, therefore enabling decision-makers to optimise patient care. We have previously applied ENTIMOS, a discrete event simulation model, to assess scenarios of patient flow and care delivery using real-life data inputs from the neurology infusion suite at Charing Cross Hospital London. The model predicted that, given current capacity and projected demand, patients would experience high-risk treatment delays within 30 months. This study aimed to address key healthcare challenges for MS patient care management as seen from a neurologist's perspective. We used ENTIMOS to predict the impact of several distinct and clinically plausible scenarios on patient waiting times at the same MS infusion suite and to quantify mitigation strategies needed to assure continuity of care. We used real-world experience of an expert neurologist to identify five clinical scenarios: (1) switching patients to a subcutaneous (SC) MS treatment of the same therapeutic agent, in the same hospital setting; (2) extending opening times to the weekend; (3) reducing the number of infusion chairs (to simulate social distancing measures applied during the coronavirus disease 2019 [COVID-19] pandemic); (4) increasing demand for infusions; and (5) increasing the scheduling approval time. Patient waiting time for next due infusion and time to high-risk treatment delays (≥ 30 days) were the main analysed model outputs. Previously published base case results were used as reference. All hypothetical scenarios were run over a 3-year horizon (with the exception of scenario 1, which was run over a 3- and 5-year horizon). Strategies to mitigate treatment delays were analysed and discussed. Switching 50% of patients to SC treatment of the same therapeutic agent administered in hospital postponed the predicted high-risk treatment delays to shortly beyond the 3-year simulation timeframe (month 38). Weekend opening reduced waiting times from 20 days to 4 days and prevented treatment delays, however, at elevated labour costs. Reducing the infusion chairs increased waiting time to 53 days on average and 86 days at the end of the simulation, leading to high-risk treatment delays within 6 months. An increased demand for infusions increased waiting time to 26 days on average and 47 days at the end of the simulation, leading to high-risk treatment delays within 22 months. Prolonged scheduling approval time did not reduce the waiting time, nor postpone the high-risk treatment delays. Decision makers need transparency on capacity constraints to better plan resourcing at infusion suites and MS treatments. Our results showed that various mitigation measures, such as increasing capacity by additional infusion chairs per year and transferring patients to other infusion suites, may help prevent treatment delays. Switching patients from IV to an SC version of the same therapeutic agent reduced the waiting time moderately and postponed high-risk treatment delays. It was insufficient to prevent high-risk treatment delays in the long term.

A Comparison of Commercially Available Digital Microscopes for Their Use in Bench-Model Simulation of Microsurgery.

Surgical education has seen a transition in the delivery of training, with increased use of online platforms to facilitate remote learning. Simulation training can increase access to education and reduce cost implications, while reducing patient risk. This study aims to compare commercially available digital microscopes, alongside a standard binocular surgical microscope, and determine whether they can be used as an alternative tool for remote microsurgery simulation. Data were collected for a total of four microscopes, including three commercially available digital microscopes, smartphone, and a binocular table microscope. Product characteristics were collated, and a subjective assessment was conducted using an 11-criteria questionnaire, graded with a 5-point scale. Results of digital microscopes were compared with the table binocular microscope.The Kruskal-Wallis test was used to compare the performance of digital microscopes to the standard binocular microscope RESULTS: The questionnaire was completed by 31 participants: two consultants, nine surgical registrars, fourteen junior trainees, and six medical students. Digital microscopes were found to be significantly more affordable and convenient for trainees; however, the cost of the smartphone was significant. Overall, the Pancellant Digital Microscope performed the poorest, with trainees commenting on its unsuitability for surgical practice; the Plugable USB Digital Microscope (PLDM) was rated overall most like the binocular table microscope. The Depth of field was shallow in all digital microscopes. With the increasing role of remote learning and simulation training in surgical education, the PLDM can provide a cheaper, more accessible alternative for junior trainees, in their pursuit of microsurgical skill acquisition.

Implementation of Nonadiabatic Molecular Dynamics for Intersystem Crossing Based on a Time-Dependent Density-Functional Tight-Binding Method.

Intersystem crossing (ISC) and internal conversion (IC) are types of nonadiabatic transitions that play important roles in a wide range of fields, including photochemistry, photophysics, and photobiology. The nonadiabatic molecular dynamics (NA-MD) method is a powerful tool for computational simulations of dynamic phenomena involving nonadiabatic transitions. In this study, we implemented the NA-MD method, which treats ISC and IC on an equal footing, where the electronic structure is treated at the level of the time-dependent (TD) density-functional tight-binding (DFTB) method, a low-cost semiempirical analog of TD density functional theory (DFT). In particular, the spin-orbit coupling calculation algorithm was implemented in the TD-DFTB framework, and the results showed trends similar to those obtained using TD-DFT. In addition, the NA-MD method successfully reproduced ultrafast ISC of 2-nitronaphthalene.

Cantilever modelling in the railway catenary shape-finding problem

Catenary designers need accurate numerical simulation tools to improve the system performance, increase operational speed, minimize wear and prevent arcing. To obtain reliable results, the catenary numerical model must avoid over-simplifying the hypotheses, which significantly affect the outcome, even if this involves increasing the model’s complexity and computational cost.While the cantilevers that support the catenary wires are frequently omitted or over-simplified in many published studies, we incorporate the cantilevers into the catenary model to examine their impact on the static and dynamic behaviour of the catenary, and to develop a shape-finding procedure to include the cantilevers by a substructuring approach.For this we compared three scenarios: (i) a conventional catenary model without cantilevers, plus two other more realistic models that include the cantilevers: (ii) considering cantilever deformation in the geometric design, and (iii) disregarding cantilever deformation in the geometric design.The findings suggest that including the cantilevers reduces the stiffness of the system and produces a similar contact force. Although the two cantilever design options analysed result in slightly different initial catenary configurations, they also yield a very similar contact force. These findings therefore validate using simplified supports, provided they are appropriately calibrated, for which the proposed comprehensive cantilever model can be applied.

Beyond simulation: Unlocking the frontiers of humanoid robot capability and intelligence with Pepper's open-source digital twin

This research paper presents a high-fidelity, open-source digital-twin of the Pepper robot developed within the framework of the Robot Operating System 2 (ROS 2) for better simulation realism in complex tasks of machine learning. We developed a dedicated, custom ROS 2 package with modern simulation tools, such as Gazebo Sim, MoveIt 2, Rviz2, that brings complete, realistic environments in line with the exact behaviors and interactions of robots in reality. Better accuracy of the physical movement of Pepper robot's simulation was shown on the digital twin, validated by the Choregraphe software and real robot performance, to be a strong platform of collaboration and further research by the community. This development greatly pushes the envelope of human-like humanoid robotics further by offering a scaled, flexible, and plausible training environment conducive to integrating complex algorithms of robot learning and interaction capabilities.

Optimal energy storage configuration to support 100 % renewable energy for Indonesia

This study presents a renewable energy (RE) optimization study to model the pathway to achieve 100 % carbon abatement, focussing on options for storage, using Indonesia's national electricity grid as a case study. Utilizing the PLEXOS energy simulation tool, the study covers the period 2021–2045. It employs an optimization of cost minimization function approach, encompassing investment, operation, maintenance, and unserved energy. The study integrates various components, including electricity supply and demand, transmission, renewable sources, and energy storage, while considering operational, build, and renewable energy target constraints. A range of scenarios are explored, varying in RE targets, battery capacities, and whether to include open-cycle gas turbines. The key novelty of this study is considering multiple versions of battery storage, with different options for the number of hours of storage. The findings indicate that higher RE targets lead to increased total installed nameplate capacity, with a significant portion from battery storage. In the early phases, batteries with 2-hour of capacity prioritized for short-term needs. As the focus shifts to more extended targets, batteries with a 4-hour capacity are recognized as more cost-effective and become the predominant choice. Over time, the least-cost strategy evolves to incorporate 10-hour capacity batteries to meet long-term energy storage requirements. To achieve a 100 % RE target by 2045, it is estimated that alongside every 100 MW of wind and solar capacity, there should be a corresponding 42 MW of energy storage. However, interpretations of these findings must consider the limited temporal resolution, uncertainties in demand and cost, and challenges related to grid inertia from energy storage, which may affect the stability and feasibility of the proposed solutions. This research offers crucial insights for energy policy and infrastructure development in renewable energy and storage system implementation.

Fractal Characterization of Simulated Metal Nanocatalysts in 3D

The surface roughness of metal nanoparticles is known to be influential toward their properties, but the quantification of surface roughness is challenging. Given the recent availability of large‐scale simulated data and tools for the computation of the box‐counting dimension of simulated atomistic objects, researchers are now enabled to study the connections between the surface roughness of metal nanoparticles and their properties. Herein, the relationships between the fractal box‐counting dimension of metal nanoparticle surfaces and structural features relevant to experimental and computational studies are investigated, providing actionable insights for the manufacturing of rough nanoparticles. This approach differs from conventional concepts of roughness, but introduces a possible indicator for their functionalities such as catalytic performance that was not previously accessible. It is found that, while it remains difficult to consistently correlate the dimension with the catalytic activity of surface facets, matching trends with their surface energy, thermodynamic stability, and number of bond vacancy are observed. This highlights the potential of fractal box‐counting dimensions to rationalize catalytic activity trends among metal nanoparticles, and opens up opportunities for the design of nanocatalysts with better performance via surface engineering.

Enterprise Malware Detection using Digital Forensic Artifacts and Machine Learning

Malware detection is a complex task. Numerous log aggregation solutions and intrusion detection systems can help find anomalies within a host or a network and detect intrusions, but they require precise calibration, skilled analysts, and cutting-edge technology. In addition, processing host-based data is challenging, as every log, event, and configuration can be analyzed. In order to obtain trusted information about a host state, the analysis of a computer’s memory can be performed, but obtaining the data from acquisition and performing the analysis can be challenging. To address this limitation, this paper proposes to collect artifacts within a network environment. This approach involves remotely gathering memory-based and disk-based artifacts from a simulated enterprise network using Velociraptor. The data was then processed using three machine learning algorithms to detect the malware samples against regular user activity generated with a user simulation tool for added realism. With this method, Random Forest and Support Vector Machine achieved a perfect classification of 41 malware samples.

Numerical simulations for the SAXO+ upgrade: Performance analysis of the adaptive optics system

SPHERE, operating at the VLT since 2014, is currently one of the high-contrast instruments with a higher performance. Its adaptive optics system, known as SAXO, will be upgraded to SAXO+, which features the addition of a second stage of adaptive optics. This stage will use a near-infrared pyramid wavefront sensor to record images of fainter exoplanets around redder stars. In this work, we compare the performance of SAXO and SAXO+. We look for the optimal values of the key system parameters of SAXO+ for various science cases and turbulence conditions. We performed numerical simulations using COMPASS, an end-to-end adaptive optics simulation tool. We simulated perfect coronagraph images of an on-axis point source, and we minimized the residual starlight intensity between 3 and $5\ / D$ as a performance criterion. The explored parameter space includes science cases (described by magnitude in G and J bands), turbulence conditions (seeing and coherence time), and key system parameters (first and second stage gains, first and second stage frequencies, pyramid modulation radius, pyramid modal gains optimization). In every science case and turbulence condition, SAXO+ reduces the residual starlight intensity inside the correction zone of the second stage by a factor of ten compared to SAXO. The optimal first stage gain is lower for SAXO+ than for SAXO alone. We quantified the gain in performance of SAXO+ when changing the second stage frequency from 2\,kHz to 3\,kHz, and we conclude that 2\,kHz may be sufficient for most realistic conditions. We give the optimal first stage gain as well as the first and second stage frequencies for every seeing, coherence time, and science case. Finally, we find that a $2\ WFS / D$ pyramid modulation radius is a good trade-off between performance and robustness against varying turbulence conditions. This study shows that the future SAXO+ system will outperform the current SAXO system in all studied cases.

Enterprise Malware Detection using Digital Forensic Artifacts and Machine Learning

Malware detection is a complex task. Numerous log aggregation solutions and intrusion detection systems can help find anomalies within a host or a network and detect intrusions, but they require precise calibration, skilled analysts, and cutting-edge technology. In addition, processing host-based data is challenging, as every log, event, and configuration can be analyzed. In order to obtain trusted information about a host state, the analysis of a computer’s memory can be performed, but obtaining the data from acquisition and performing the analysis can be challenging. To address this limitation, this paper proposes to collect artifacts within a network environment. This approach involves remotely gathering memory-based and disk-based artifacts from a simulated enterprise network using Velociraptor. The data was then processed using three machine learning algorithms to detect the malware samples against regular user activity generated with a user simulation tool for added realism. With this method, Random Forest and Support Vector Machine achieved a perfect classification of 41 malware samples.

Internal and external factors affecting the performance score of surgical trainees doing laparoscopic appendectomy: a prospective, observational cohort study in a structured training programme.

Laparoscopic appendectomy is a common procedure and introduced early in general surgical training. How internal (i.e. surgeon's experience) or external (i.e. disease severity) may affect procedure performance is not well-studied. The aim of this study was to evaluate factors that may have an influence on the performance scores for surgical trainees. A prospective, observational cohort study of laparoscopic appendectomies performed by surgical trainees (experience < 4years) operating under supervision. Trainers evaluated trainees' overall performance on a 6-point scale for proficiency. Perioperative data were recorded, including appendicitis severity, operating time and the overall difficulty of the procedure as assessed by the trainer. A "Challenging" procedure was defined as a combination of either/or "perforation" and "difficult". Trainees who had performed > 30 appendectomies were defined as "experienced". The trainees were asked if they had used simulation or web-based tools the week prior to surgery. 142 procedure evaluation forms were included of which 19 (13%) were "perforated", 14 (10%) "difficult" and 24 (17%) "Challenging". Perforated appendicitis was strongly associated with procedure difficulty (OR 21.2, 95% CI 6.0-75.6). Experienced trainees performed "proficient" more often than non-experienced (OR 34.5, 95% CI 6.8-176.5). "Difficult" procedures were inversely associated with proficiency (OR 0.1, 95% CI 0.0-0.9). In "Challenging" procedures, identifying the appendix had lowest proficiency (OR 0.4, 95% CI 0.1-0.9). The procedures assessed as "difficult" had significantly longer operating time with a median (IQR) of 90 (75-100) min compared to 59 (25-120)min for the non-difficult (p < 0.001). Both internal and external factors contribute to the performance score. Perforated appendicitis, technical difficult procedures and trainee experience all play a role, but a "difficult" procedure had most overall impact on proficiency evaluation.

SIMULATION ANALYSIS TO FORECAST PROGRESS AND OPTIMIZE RESOURCE UTILIZATION FOR MATERIAL TRANSPORTATION ACTIVITIES IN THE CONSTRUCTION PROJECT ON PHU QUY ISLAND

This article presents the main results of using a simulation tool to predict time and optimize the resource utilization for the transportion of materials processes in the construction project on Phu Quy island. A simulation model, which includes activities of material transfer processes, has been established and developed based on the EZStrobe simulation program. Various simulation scenarios with different resource usage options have been analyzed. The simulation results are also compared and evaluated with real-world project data. It indicates that for both productivity, time and cost, the option of using two 50-ton cranes at the dock; two 25-ton cranes at the storage yard and four trucks will be the best solution. The results of the study demonstrated the effectiveness of the simulation program, which may help managers choose the resource allocation plan and apply necessary changes to obtain the optimum results in terms of production and cost efficiency for the transportion of contruction materials on the island.