Simulation Tool Research Articles

The use of computer modeling in the educational process based on the example of studying Coulomb’s law

Abstract The study analyzes computer modeling tools intended for use in educational and scientific contexts. The concept of modeling as a method of research and a teaching tool is examined, the emphasis is placed on computer modeling. It is shown how physical laws, particularly Coulomb’s law, can be studied through modeling. The study uses pre-made simulators, spreadsheets, mathematical packages, as well as our own software in Object Pascal and Python. Experimental testing has confirmed the effectiveness of computer modeling in teaching natural sciences. A survey of students and teachers showed interest in further use of simulation tools in the educational process.

Extension of the GISSMO fracture model for thin-walled structures under combined tensile and bending loads

Ductile fracture prediction for thin-walled structures requires computationally efficient simulation tools able to approximately represent the key effects that occur on the sub-thickness scale. Unfortunately, classical shell elements, typically used to model deformation and failure of thin components, are inherently in a state of plane stress and therefore unable to capture the through-thickness stress distribution. This is consequential considering recent studies that demonstrated the differences between fracture in bending vs. in-plane tension. A laterally constrained thin metal plate under in-plane tension is likely to fracture under significantly lower plastic strain than the same plate under bending (with fracture initiating on the tensile side), even though both these conditions are examples of plane strain tension. Under in-plane tension fracture is preceded by a through-thickness neck, and therefore higher stress triaxiality than in the case of plane strain bending, where necking is absent. To account for these differences, we propose an extension of the GISSMO model, which relies on a simple fracture criterion based on stress-dependent fracture strain defined by the user (i.e. fracture locus). The fracture strain is typically determined experimentally using a combination of in-plane tensile tests under varying degree of lateral constraint and shear tests. The proposed extension involves defining a separate fracture locus for bending, also determined experimentally using bending tests. Fracture occurs when the equivalent plastic strain reaches its critical level represented by interpolation between the two bounding cases, i.e. bending and in-plane tension, with a bending index Ω used as an interpolation parameter.

Characterization of weakly nonlinear effects in relationship to transducer parameters in focused ultrasound therapy.

Focused ultrasound therapy has been widely used for the treatment of various diseases, employing different types of transducers. The focused ultrasound pressure fields inevitably exhibit nonlinear effects, which can influence the ablation region. However, the nonlinear effects exhibit noticeable variations across different applications. The characterization of the nonlinear pressure fields of ultrasound is important for the effective implementation of focused ultrasound therapy. The traditional angular spectrum method (ASM) was extended to accurately and efficiently simulate the propagation of weakly nonlinear ultrasound in heterogeneous mediums of clinical model. The nonlinear effects were further analyzed in relationship to the transducer parameters that are different in various applications. The pressure fields were simulated using the extended ASM, incorporating calculations for phase aberration in the frequency domain and magnitude compensation in the spatial domain to account for heterogeneous acoustic impedance mismatch. Validation was performed by comparison to k-Wave simulation results using two simplified clinical models, an abdominal soft tissue and a transcranial skull model. The nonlinear effects were then analyzed in relation to the transducer parameters of f-number and effective source area based on the same acoustic output power. The analysis of nonlinear effects was conducted under both homogeneous medium and the clinical models. The simulation results demonstrated a maximum error of 3.93% in the calculated harmonic pressure of the abdominal model, and a maximum error of 4.89% within the transcranial model when comparing the extended ASM simulation results to those obtained from k-Wave simulations. The characterization of the nonlinear effects reveals a strong correlation with the transducer parameters. Specifically, the results indicate that the nonlinear effects intensify with an increase in the effective source area and f-number, under the same acoustic output power of the transducer. However, the clinical model also showed an influence on the nonlinear effects in relation to the f-number. The extended ASM was demonstrated as an accurate and efficient simulation tool, and the simulation results provide a reference for evaluating the intensity of nonlinear effects in various transducer designs.

Enhancing Long-Term Water Quality Modeling by Addressing Base Demand, demand Patterns, and Temperature Uncertainty Using Unsupervised Machine Learning Techniques.

Water quality modelling in Water Distribution systems (WDS) is frequently affected by uncertainties in input variables such as base demand and decay constants. When utilizing simulation tools like EPANET, which necessitate exact numerical inputs, these uncertainties can result in inaccurate simulations. This study proposes a novel framework that leverages unsupervised machine learning, specifically a Gaussian Mixture Model (GMMs), to represent and integrate these uncertainties in the simulation. By classifying historical water demand into fuzzy clusters, the framework allows for certain linguistic inputs (e.g., "high" or "low" demand) to be used in water quality simulations. The framework also incorporates representative hourly demand patterns and temperature-dependent chlorine decay constants based on historical data correlations. Validations were conducted on the Anytown network using WNTR-EPANET, comparing simulated chlorine residuals with Validation data from 181 steady-state simulations. The simulation through the framework achieved a Jensen-Shannon Divergence (JSD) of less than 0.008 across all demand clusters, indicating high similarity between predicted and actual probability distributions . In comparison to other simulation scenarios tested, which exhibited increased variability (JSD > 0.18), the proposed framework demonstrated improved accuracy in representing chlorine residual distributions. The methodology is adaptable to other systems, if similar historical datasets containing key variables, such as flow rates and temperature, are provided. While the framework offers a more flexible and accurate approach to handling uncertainties in WDS, its effectiveness is contingent upon the availability of robust historical demand and temperature data for decay constant calibration.

Organizational culture and leadership style in Spanish Hospitals: Effects on knowledge management and efficiency

Hospital culture and leadership style have attracted considerable attention in research, with compelling evidence indicating their potential competitive advantages, including their crucial role in ensuring the successful implementation of knowledge management and its impact on hospital efficiency. The aim of this paper is to identify the effects of organizational culture and leadership style on knowledge management and hospital efficiency. Fuzzy cognitive maps (FCMs) are relational models that can be used to represent the opinions and knowledge of expert to infer cause-effect relationships among different concepts. The use of FCMs as a simulation tool enables the evaluation of potential scenarios based on different organizational cultures and leadership styles in hospitals. Developing an FCM for this study involved several steps. Firstly, data were collected through interviews with 21 experts in hospital management. The interviews were conducted between May and September 2023 either face-to-face or via videoconference. Once individual cognitive maps had been created, consensus among them was achieved through a multicriteria decision-making process, wherein the expert opinions were averaged. The separate cognitive maps of each expert were then integrated to produce a single FCM using the augmented FCM approach. Reflecting expert insights from the FCM, hospitals with a hierarchy culture exhibit diminished levels of knowledge creation, management, and overall hospital efficiency, whereas those with an adhocracy culture show improvements in knowledge creation, knowledge exploitation, and overall hospital efficiency in comparison to alternative ones. From the experts' FCM perspective regarding leadership style, transformational leadership achieves the highest level of knowledge management and hospital efficiency in hospitals with an adhocracy culture. Finally, this paper offers a reference for practising knowledge management and improving hospital efficiency through adhocracy culture and transformational leadership.

CO2 solubility and physicochemical properties of 2-amino 2-methyl-1-propanol based solvents for post-combustion CO2 capture. Predictive modeling using machine learning

Global warming, caused by Carbon Dioxide (CO2) emissions from hard-to-abate industries, necessitates CO2 capture as one of the promising alternatives. Post-combustion CO2 capture (PCC) can be retrofitted for industries where chemical absorption using an amine-based solvent is an efficient method for CO2 capture. An essential factor for CO2 absorption in an amine mixture is the study of CO2 solubility and CO2 + Amine physicochemical properties to identify the more efficient solvents. Often experimental data are available in the literature, but precisemodeling is necessary to use such data for further utilization in a process-plant model. In this work, a blended solvent aqueous 2-amine-2methyl-1-Propanal (AMP) with Piperazine (PZ) and 1-(2-aminoethyl) piperazine (AEP) have been identified as a potential candidate for PCC application. The fundamental properties such as the density, viscosity and CO2 solubility in AMP + PZ and AMP + AEP blended solutions have been predicted by employing machine learning to get an exceptionally efficient model. This data-mining technique efficiently calculates solubility, density, and viscosity by using 691, 559, and 559 experimental data sets respectively, from the literature of AMP + PZ. Similarly, 120 data sets of solubility have been collected from the literature for AMP + AEP. Random Forest regression, Artificial Neural Network (ANN), and XGBoost regression algorithms are adapted to predict CO2 solubility in aqueous AMP blended solution. The effectiveness of these models has been confirmed through statistical analysis, leading to the conclusion that XGBoost outperforms the other two approaches. Moreover, an identical approach has been employed to develop a predictive model for the density and viscosity of AMP + PZ solvent. The outcomes imply that the machine learning algorithm performs better with experimental data and may be utilised as an effective simulation tool.

Assessing the impacts of air-sealing on the sizing, operation, and economic feasibility of ground-source heat pumps for electrifying single-family houses in the US

According to recent studies and reports, in single-family houses (SFHs), air-sealing can significantly lower the thermal loads for space heating and cooling. Thus, air-sealing in SFHs could reduce the required size and cost of ground source heat pump (GSHP) systems for electrifying SFHs. This study investigated the costs and benefits of integrating air-sealing with GSHPs for retrofitting existing SFHs when compared with air-source heat pumps. A whole building energy simulation tool integrated with an advanced design tool for modeling ground heat exchangers was used to calculate changes in required GSHP capacity, total borehole length, and building energy consumption with and without air-sealing in SFHs in 16 US climatic regions. The results from this study showed that reducing outdoor air infiltration from 0.8 air changes per hour (ACH) to the minimum ventilation requirement (0.35 ACH) can significantly reduce borehole length (up to 55%), GSHP capacity (up to 48%), and total heating electricity reduction, especially in cold climates (up to 44%). The results also showed that for airtight homes (0.03 ACH infiltration) with a direct outdoor air system, the minimum required borehole length, GSHP capacity, and total heating electricity consumption can be reduced up to 70%, 68%, and 67%, respectively, when compared with SFHs with 0.8 ACH infiltration. Moreover, the life cycle cost analysis showed that air-sealing in conjunction with a GSHP is more profitable than replacing the existing system with an air-source heat pump, even without any incentives for most climatic regions in the US (except for some hot regions).

Development of an FDD model for an existing building using transfer learning

Building heating, ventilation, and air conditioning (HVAC) systems are affected by several errors that can cause thermal discomfort to occupants and waste energy in buildings. Therefore, early and accurate Fault Detection and Diagnosis (FDD) is vital for enhancing the system’s efficiency. Data-driven FDD is promising because it is convenient compared to the first-principles-based rule set that demands in-depth expertise. However, to realize data-driven FDD for real-life cases, the data-imbalance problem in FDD must be solved. In this study, the authors suggest a novel approach that generates synthetic data from a building system simulation tool, HVACsim+, using them as a source model to apply transfer learning to a target air handling unit system. Even though from the existing target system only the normal operational data were available, the transfer learning approach was satisfactory, confirming that the proposed method effectively mitigates data imbalance in developing data-driven FDD.

Quantifying the external costs of autonomous on-demand ride pooling services

Mobility On Demand (MOD) services, such as ride-pooling, provide convenient and cost-effective transportation options. While previous studies focused on operational costs and service quality, we take a broader perspective by examining the external costs associated with autonomous ride-pooling services. Incorporating external costs into the design and evaluation of MOD services enables a comprehensive understanding of their impact on the entire urban population, informing effective regulations and incentives. We present an approach for calculating space-varying external costs, accounting for factors like air pollution, climate impact, noise and accidents. These costs are integrated into FleetPy, an agent-based simulation tool for ridesharing analysis and optimization. A case study in Munich uncovers the tradeoffs between external costs, internal costs, and service quality. Our findings suggest that mid-sized vehicles with a three-person capacity strike a balance between energy efficiency and transport capacity. By applying our approach, external costs can be reduced by up to 37%.

ANALYSIS OF THE CROWD MANAGEMENT AND PEDESTRIAN MOVEMENT DURING HAJJ PILGRIMAGE ON MAKKAH

The Hajj pilgrimage, the world's largest annual mass gathering, poses significant challenges in crowd management and pedestrian movement due to the sheer number of participants and logistical complexities. This study emphasizes the need for effective strategies to ensure the safety of millions of pilgrims in Mecca, Saudi Arabia. By reviewing literature and analysing pedestrian movement systems, it identifies key bottlenecks and safety risks, especially during high-density periods like Nafra day. Field observations from 2019 to 2024, including the post-COVID-19 era, offer insights into crowd behaviour and the effectiveness of management strategies. The study highlights the importance of integrating advanced simulation tools with urban design to optimize pedestrian pathways and prevent overcrowding, contributing to Saudi Vision 2030's goals of enhancing the pilgrimage experience and ensuring participant safety.

Engineering Origami: A Systematic Literature Review in the context of Space Technology, Design, and Applications

Abstract: Origami is the ancient art of Japanese paper folding. For years it has been used to create stunning works of art. But origami has also been used in more surprising ways, like car airbags, stents, and even space exploration. Origami is a bit more than just a paper crane. Engineers use it for use, solve the interesting problems. Origami teaches how to fold very large thin sheets into very small spaces. And that's exactly what space engineers need. The reason for incorporating origami for space applications is to launch structures in space that are just amazingly big using flashers, starshade folding, tessellations, etc. So being able to fold these structures such that they fit neatly within our rocket and then unfold when they get to space. The researcher has attempted to do a literature review and to demonstrate how origami can be used in aerospace engineering and related fields. Innovative aerospace solutions such as morphing aircraft wings and deployable space constructions are made possible by the principles of origami. Using sophisticated modelling and simulation tools is essential for creating intricate origami structures for aerospace applications.

Application of BIM and Virtual Reality of System Integration Design and Development in Medical Building Projects: A Case Study in China

Design outcomes in the Medical Building Projects (MBPs) must satisfy the demand of the medical staff and stakeholders. However, they often do not have the relevant engineering expertise, so the design team is prone to misunderstandings when communicating with them using traditional two-dimensional (2D) Computer-Aided Design (CAD) drawings. The medical staff may not readily visualize and understand the design and then provide feedback to the designer through professional medical requirements or advice, which results in the need to redesign and carry out additional work during the design phase, thus delaying the progress of the MBPs. To address these challenges, this study aims to develop an integrated system based on BIM, advanced rendering engines, and VR technology for use in non-immersive and immersive VR environments. Moreover, in this study, the integrated system was applied to the infection-building project of the People's Hospital of Qingbaijiang District, Chengdu City, and semi-structured interviews were distributed to 16 engineers, designers, contractors, and medical staff who had been involved in the whole project to validate the practicality and effectiveness of the integrated system. The results indicate that this system integration is an efficient visual communication and simulation tool in medical design. Key advantages include enhanced communication efficiency between the design team and medical stakeholders, improved visual interaction, and streamlined decision-making processes within a 3D VR/BIM environment. The successful implementation of this integrated system is expected to significantly aid design teams and stakeholders in effectively managing medical design tasks in future projects.

AERODYNAMIC AND ROLLING RESISTANCES OF HEAVY DUTY VEHICLE. SIMULATION OF ENERGY CONSUMPTION

The main objective of the work was to develop a comprehensive model of energy consumption simulation of heavy duty vehicles using the VECTO simulation tool. The research issue was the impact of aerodynamic drag and rolling resistance on fuel consumption and emissions under various driving conditions described in four driving cycles: Urban Delivery, Regional Delivery, Urban, and Suburban. Each cycle differed in driving time, distance and average speed to represent different operational scenarios. The methodology involved defining vehicle parameters such as weight, aerodynamic coefficients and tyre rolling resistance. The main findings show that the impact of both aerodynamic drag and rolling resistance on fuel consumption can be efficiently modelled. It has been proven that the proposed modifications to aerodynamic drag and rolling resistance can reduce fuel consumption by more than 8%. The lowest fuel consumption was achieved in the Regional Delivery cycle, while the Urban cycle had the highest fuel consumption due to frequent vehicle stops. The results show that optimization of vehicle design and its performance can significantly improve energy efficiency and reduce emissions. A computational modelling tool such as VECTO can contribute to sustainable transport solutions and improve the efficiency of heavy duty vehicle.

Comprehensive data security and compliance framework for SMEs

Small and Medium-sized Enterprises (SMEs) are increasingly relying on cloud platforms to support critical business operations, making effective disaster recovery (DR) strategies essential for ensuring business continuity. This review proposes a robust disaster recovery framework tailored for SMEs, designed to minimize downtime and data loss in the event of a system failure, cyberattack, or natural disaster. The framework integrates advanced cloud technologies to create a cost-effective, scalable solution that aligns with the resource constraints of SMEs while providing enterprise-grade resilience. Key components of the disaster recovery framework include cloud-based data replication, automated backup solutions, and geo-redundant storage to ensure that data is continuously available and recoverable. This model employs real-time data synchronization and incremental backups to minimize Recovery Point Objectives (RPO), ensuring that critical data is not lost during an unexpected outage. Additionally, the framework leverages automated failover mechanisms to achieve low Recovery Time Objectives (RTO), allowing businesses to restore operations quickly after an interruption. Cloud orchestration tools such as AWS Elastic Disaster Recovery or Azure Site Recovery are utilized to automate disaster recovery processes, reducing manual intervention and improving the speed of recovery. The framework also incorporates regular testing of disaster recovery plans, using simulation tools to identify weaknesses and optimize response times. For SMEs, cost-effectiveness and ease of management are crucial. The framework emphasizes a pay-as-you-go model for cloud resources, allowing businesses to scale their disaster recovery solutions as they grow without incurring excessive upfront costs. By providing continuous monitoring and proactive threat detection, this disaster recovery framework ensures that SMEs can maintain uninterrupted business operations on cloud platforms, thereby enhancing resilience and mitigating the financial and operational risks associated with data loss and system downtime.

Assessing wind turbine blade durability longevity utilizing national renewable energy laboratory tools

With the escalating significance of renewable energy, there exists a pressing demand for precise software and tools capable of designing and forecasting wind turbine blade service life. OpenFAST, TurbSim, and MLife are pivotal open-source wind turbine simulation tools that integrate aerodynamics, structural dynamics, wind simulation, and control systems. They enable accurate prediction of wind turbine performance. This study aims to leverage these software resources to model turbulent wind conditions using predefined basic parameters and then calculate fatigue and longevity of the wind turbine blade to determine service life. Particularly, MLife was employed for rose loading, enhancing the fidelity of the loading cycle simulation. Collaboration between the National Renewable Energy Laboratory and the research team played a crucial role in establishing a foundational baseline and implementing realistic input parameters for evaluating the ultimate load, determining damage equivalent loads (DEL), and projecting service life. In all simulations, a constant factor of safety (FOS) of 1.5 was consistently applied. Additionally, Finite Element Analysis (FEA) was employed to validate our numerical experiments, ensuring the accuracy of the obtained results by correlating simulation outcomes with real-world scenarios, further strengthening the credibility of the study.

A Framework for Mitigating Gray Hole Attack On Mobile Ad Hoc Networks

posing a significant challenge to network performance and reliability. In a gray hole attack, a malicious node selectively drops packets while forwarding others, leading to increased packet loss and latency. This paper presents a novel Gray Hole Prevention algorithm designed to detect and mitigate the effects of such attacks, thereby enhancing the security and efficiency of MANETs. The proposed algorithm integrates existing techniques, specifically the Secure Detection Prevention and Elimination Gray Hole (SDPEGH) approach, with proactive measures to improve detection accuracy and minimize the risk of blacklisting legitimate nodes. Extensive simulations were conducted using the NS-2 simulation tool, comparing the performance of the proposed algorithm against traditional methods. The results demonstrate a significant improvement in network performance, with the GRAY-HP algorithm achieving a PDR of 92%, compared to 78% for the SDPEGH algorithm, representing an increase of 18%. Additionally, the throughput improved by 25%, reaching 85 kbps, while the energy consumption was reduced by 15%, indicating a more efficient use of resources. The routing overhead was also minimized by 20%, showcasing the algorithm's effectiveness in maintaining network stability. This research contributes to the field of network security by providing a robust solution to gray hole attacks, thereby enhancing the reliability and performance of MANETs. The findings underscore the importance of proactive security measures in dynamic and decentralized environments, paving the way for future advancements in secure communication protocols for mobile networks.

Early-stage design support for ice-shell architecture with integrated snow drift simulation tool

The data on snow distribution on roofs can aid in optimizing the weatherability of ice-shell architecture. However, there is currently a lack of technology for predicting snow drift in early-stage design. The study proposes a snow drift simulation method for ice shells, utilizing Butterfly to call the OpenFOAM solver. A snow drift simulation tool based on this method was developed and validated through three case studies. The validation results provide suggestions for mesh accuracy and time steps for typical ice-shell architecture simulation with the tool. Simulation results from the tool are used to analyze the impact of ice-shell architectural specific features (texture, plane, orientation) on weatherability, focusing on the coupling optimization of snow and radiation for typical ice shells. The study demonstrates the tool's effectiveness, simplifying the simulation process and extending its applicability to other cold-region buildings. The feature analysis indicates that the ribbed texture more effectively blocks solar radiation through snow distribution.

A Review of Myoelectric Prosthetic Arm Designs from 2011 to 2022

Prosthetic arms are worn by people whose arms have been amputated. Amputation involves surgical removal of the muscle, neurosensory system, and skeletal system. Myoelectric prosthetics make use of the signals generated by the intact muscles for the limb movement. Prosthetic limbs are designed using mechanical parts with suitable gear and motors. Users of prosthetic arms can carry out tasks associated with everyday living and their jobs almost exactly like they would with a natural arm. Prosthetic arms come in a variety of designs based on the needs of the user. The current research reviews the goals, plans, and trials conducted on prosthetic elbows, wrists, and hands between 2011 and 2022 from reputable conferences and journals. In this work, the design of two prosthetic hands with one degree of freedom and thirteen degrees of freedom, respectively, in the 3D simulation tool SolidWorks is described. Prosthetic elbow and a wrist with one degree of freedom were designed as well. The fine motor activities can be performed with the prosthetic fingers designed in this work.

Real-Time Simulation and Hardware-in-the-Loop Testing Based on OPAL-RT ePHASORSIM: A Review of Recent Advances and a Simple Validation in EV Charging Management Systems

Phasor-domain (RMS) simulations have become increasingly vital in modern power system analysis, particularly as the complexity and scale of these systems have expanded with the integration of renewable energy sources. ePHASORSIM, an advanced phasor-based simulation tool developed by OPAL-RT, plays a crucial role in this context by enabling real-time phasor-domain simulation and hardware-in-the-loop testing. To keep pace with these evolving needs, continuous efforts are being made to further improve the accuracy, efficiency, and reliability of ePHASORSIM-based simulations. These efforts include automating model conversion processes for enhanced integration with ePHASORSIM, extending ePHASORSIM’s simulation range with custom models, developing hybrid co-simulation techniques involving ePHASORSIM and an EMT simulator, enhancing simulation scalability, and refining HIL testing to achieve more precise validation of control and protection systems. This paper provides a comprehensive review of these recent advances. Additionally, the paper discusses the conversion of models from PowerFactory—a widely used and comprehensive modeling environment—to ePHASORSIM through both automated tools and manual methods using Excel workbooks, which has been discussed little in the literature. Furthermore, as ePHASORSIM is a relatively new tool with limited cross-validation studies, the paper aims to contribute to this area by presenting a comparative validation against DIgSILENT PowerFactory, with a specific emphasis on its application in electric vehicle charging management systems.

Comprehensive review of literature on Parkinson’s disease diagnosis

PD is one of the neurodegenerative illnesses affects 1–2 individuals per 1000 people over the age of 60 and has a 1 % prevalence rate. It affects both the non-motor and motor aspects of movement, including initiation, execution, and planning. Prior to behavioral and cognitive abnormalities like dementia, movement-related symptoms including stiffness, tremor, and initiation issues may be observed. Patients with PD have substantial reductions in social interactions, quality of life (QoL), and familial ties, as well as significant financial burdens on both the individual and societal levels. The healthcare industry is mostly using ML approaches with the modalities like image, signal, and data as well. Therefore, this survey aims to conduct a review of 50 articles on Parkinson disease diagnosis using different modalities. The survey includes (i) Classifying multimodal articles on Parkinson disease diagnosis (image, signal, data) using various machine learning, deep learning, and other approaches. (ii) Analyzing different datasets, simulation tools used in the existing papers. (iii)Examining certain performance measures, assessing the best performance, and chronological review of reviewed paper. Finally, the review determines the research gaps and obstacles in this research topic.