Simulation-based Study Research Articles

Neural network-based surrogate modeling and optimization of a multigeneration system

Multi-Objective Optimization (MOO) poses a computational challenge, particularly when applied to physics-based models. As a result, only up to three objectives are typically involved in simulation-based optimization. To go beyond this number, Surrogate Models (SMs) need to replace such high-fidelity models. In this exploratory study, the objectives are to perform comprehensive regression surrogate modeling and to conduct MOO for a Multi-Generation System (MGS). The most suitable SM was chosen among four neural-network models: Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and an ensemble model developed through brute-force search using the three aforementioned models. The final model was found to be superior to others, achieving R2 values ranging from 0.9830 to 0.9999. Next, an optimization problem with six conflicting objectives was defined and performed at four distinct values of Direct Normal Irradiation (DNI), a time-dependent feature. This aimed to provide multi-criteria decision-making information based on atmospheric transparency. As a result, new understandings were gained: (I) exergy efficiency, production cost, and freshwater production rate were found to be highly influenced by DNI, and (II) the critical range of operation was observed within the DNI interval of 100 to 400 W/m2. Furthermore, we compared the result of the six-objective optimization with that of the bi-objective optimization obtained in our simulation-based study and found that all objectives showed improvements ranging from 1.9% to 12.7%. Finally, based on the findings obtained in the present study, some practical recommendations were put forward for applying the proposed methodology to similar MGSs.

Integrating connected autonomous shuttle buses as an alternative for public transport – A simulation-based study

The concept of connected autonomous shuttles is gaining recognition for its potential to revolutionize traditional public transport by offering a safer and more consistent travel experience. Despite technological advancements facilitating their integration into current traffic systems, research in this area is still exploratory, with public acceptance and trust in autonomous technology posing significant challenges. This paper contributes to the field by validating the benefits of deploying connected autonomous shuttles and examining their impact on ridership via an immersive experience. We introduce a VR-enabled co-simulation strategy to analyze the effects of replacing a traditional bus service line with connected shuttles in Melbourne, Australia. Our case study results reveal that while autonomous shuttles can reduce travel time because of optimized vehicle motion, their traffic efficiency is affected by fleet size, with the optimal fleet size identified as four in the study area. Furthermore, we observed a mismatch between participants’ stated intentions and actual boarding behavior, indicating that the attractive appearance of this novel mobility mode may not necessarily enhance ridership. Our work offers an alternative approach to simulation studies in futuristic public transportation and complement existing literature in the field.

Blooming and pruning: learning from mistakes with memristive synapses

Blooming and pruning is one of the most important developmental mechanisms of the biological brain in the first years of life, enabling it to adapt its network structure to the demands of the environment. The mechanism is thought to be fundamental for the development of cognitive skills. Inspired by this, Chialvo and Bak proposed in 1999 a learning scheme that learns from mistakes by eliminating from the initial surplus of synaptic connections those that lead to an undesirable outcome. Here, this idea is implemented in a neuromorphic circuit scheme using CMOS integrated HfO2-based memristive devices. The implemented two-layer neural network learns in a self-organized manner without positive reinforcement and exploits the inherent variability of the memristive devices. This approach provides hardware, local, and energy-efficient learning. A combined experimental and simulation-based parameter study is presented to find the relevant system and device parameters leading to a compact and robust memristive neuromorphic circuit that can handle association tasks.

Exploration of Linearity Analysis in Nanotube GAA MOSFET Through Simulation-Based Study Utilizing Multi-Material Gate Technique

Exploration of Linearity Analysis in Nanotube GAA MOSFET Through Simulation-Based Study Utilizing Multi-Material Gate Technique

Port Access Fluidity Management during a Major Extension Project: A Simulation-Based Case Study

The increasing demand for freight services and the use of larger vessels to meet this demand has led to challenges related to storage space and logistics activities, highlighting the need for improvements in port infrastructure for better logistics management. At a crucial phase in its growth, the Port of Trois-Rivières in Canada is planning a major expansion, including the construction of a new terminal to enhance its hosting capacities and freight services. This expansion faces potential access congestion problems during the planned construction, exacerbated by the port’s urban setting. In response to the needs identified by the port authorities for this event, the study’s objective is to assess the implications of increased construction and freight truck flows on access gate fluidity and the impact of additional access infrastructure investment to mitigate potential congestion. These evaluations aim to define effective access management strategies throughout the construction period of the new terminal. To address these complexities, our approach is based on scenario analysis in variants co-constructed with the partner. These scenarios are evaluated using simulation models, configured according to parameters calibrated with a granularity that allows congestion detection. The results enabled an evaluation of the capability of existing and potential gates to manage access. Subsequently, recommendations were shaped in accordance with the expected objectives to manage access traffic effectively. These recommendations concern the optimization of construction activity planning, the layout and planning of access, and the importance of enhanced collaboration between municipal and port authorities for more controlled road traffic management. Recognizing the importance of synchromodality, road network centrality management, and the outsourcing of capacity through inter-port cooperation and with dry ports to manage congestion, these tools will be discussed in this work. The study proposes an approach that reconciles scientific rigor with the implementation constraints of the proposed solutions, allowing this study wider applicability in various port contexts facing challenges in this field of study.

Numerical Simulation-Based Study on the Microstructure of TC4 Titanium Alloy in CMT Additive Manufacturing

This thesis explores the thermal and mechanical phenomena occurring during the Cold Metal Transfer (CMT) additive manufacturing process of TC4 titanium alloy. The investigation delves into the effects of layer-by-layer deposition on the thermal cycles, stress distribution, and microstructural evolution of the additive manufactured specimens. Key findings include the identification of thermal accumulation with increasing deposition layers, leading to a decrease in cooling rates and a slight increase in the peak molten pool temperatures. The temperature profiles on the substrate exhibit cyclic patterns of peaks and troughs, which become smoother and exhibit less variance between maximum and minimum temperatures as deposition progresses. Stress analysis reveals that equivalent stress within the deposited layers decreases with the number of layers. This is attributed to the thermal stress accumulation and stress cycles induced by multiple thermal cycles, which initially increase the equivalent stress. However, a reduction in cooling rates over time and the effects of remelting facilitate the release of interlayer stress, resulting in minimal residual stress within the cooled specimens. Microstructurally, the TC4 titanium alloy specimens are characterized by the presence of primary β phase spanning across multiple deposition layers, growing in the direction of additive manufacturing. The accumulated heat input from increased layering enhances the growth of the primary β phase.The specimens' microstructure features a basketweave pattern comprised of α' martensite, α, and β phases, with α' martensite directly transformed from the primary β phase. Repeated heating cycles foster diverse pathways for the secondary α phase's formation, leading to more pronounced, directionally parallel α phases and extensive basketweave structures within the specimen layers. Higher temperature gradients at specimen edges encourage the formation of needle-like α' martensite. This thesis contributes to the understanding of the thermal behavior, stress dynamics, and microstructural characteristics in TC4 titanium alloy during CMT additive manufacturing, highlighting the interplay between process parameters, thermal management, and material response.

Design–operation gap caused by parameter variance in HVAC system control sequences: A simulation-based study on energy efficiency and temperature controllability

During the design phase, the annual energy consumption of heating, ventilation, and air-conditioning (HVAC) systems is frequently simplified or fixed under certain assumptions due to uncertain parameters within the control sequence, resulting in a performance gap between the intended and actual performances. This study assessed the impact of six uncertain parameters on system energy consumption and indoor temperature control under various updating scenarios. First, sensitivity analysis was conducted using the one-at-a-time method to identify the influential impact of the single uncertain parameter. Then, mutual impact analysis was conducted to showcase how different parameters affect each other, and mixed impact analysis was applied to evaluate how intended design performance converges to actual operating outcomes. A detailed physical model of a single-zone variable air volume system was developed to enable a comprehensive evaluation across different operational conditions. The results indicate that a 10% change in some parameters can lead to a 20∼30% increase in energy consumption, especially during low-load winter months. Properly evaluating the influence of one parameter is difficult without taking the effects of additional parameters into account, and some combinations of parameters can significantly decrease the system performance. These results underscore the necessity of considering the updating and combination scenarios of different parameters within the control sequences during the design phase.

Empirical validation of federated learning with YOLO v7-tiny for road sign detection: A simulation-based comparative study

Detecting road signs is a critical component in the development of intelligent driving systems. While centralized machine learning approaches have demonstrated potential in this field, the untapped potential of Federated Learning warrants exploration. This research aims to bridge this gap by examining the feasibility of applying Federated Learning within edge Artificial Intelligence (AI) computing environments for the purpose of road sign detection. Utilizing the You Only Look Once (YOLO) v7-tiny model and a range of experimental parameters demonstrates that Federated Learning is viable and outperforms centralized approaches under specific conditions. The study's empirical analysis highlights the sensitivity of detection accuracy to varying experimental parameters. The study contributes to the existing literature by establishing the efficacy of Federated Learning in road sign detection, particularly in edge AI settings constrained by hardware limitations and privacy concerns. However, the study acknowledges limitations, including the lack of deployment on actual edge AI devices and a restricted range of experimental parameters. Future research should aim for more exhaustive experiments with broader datasets, diverse parameters, and real-world edge AI environments. These findings offer valuable insights for future implementations in intelligent automotive systems.

Optimal Social and Vaccination Control in the SVIR Epidemic Model

In this paper, we introduce an approach to the management of infectious disease diffusion through the formulation of a controlled compartmental SVIR (susceptible–vaccinated–infected–recovered) model. We consider a cost functional encompassing three distinct yet interconnected dimensions: the social cost, the disease cost, and the vaccination cost. The proposed model addresses the pressing need for optimized strategies in disease containment, incorporating both social control measures and vaccination campaigns. Through the utilization of advanced control theory, we identify optimal control strategies that mitigate disease proliferation while considering the inherent trade-offs among social interventions and vaccination efforts. Finally, we present the results from a simulation-based study employing a numerical implementation of the optimally controlled system through the forward–backward sweep algorithm. The baseline model considered incorporates parameters representative of typical values observed during the recent pandemic outbreak.

Solar-Assisted Carbon Capture Process Integrated with a Natural Gas Combined Cycle (NGCC) Power Plant—A Simulation-Based Study

In the realm of Natural Gas Combined Cycle (NGCC) power plants, it is crucial to prioritize the mitigation of CO2 emissions to ensure environmental sustainability. The integration of post-combustion carbon capture technologies plays a pivotal role in mitigating greenhouse gas emissions enhancing the NGCC’s environmental profile by minimizing its carbon footprint. This research paper presents a comprehensive investigation into the integration of solar thermal energy into the Besmaya Natural Gas Combined Cycle (NGCC) power plant, located in Baghdad, Iraq. Leveraging advanced process simulation and modeling techniques employing Aspen Plus software, the study aims to evaluate the performance and feasibility of augmenting the existing NGCC facility with solar assistance for post-carbon capture. The primary objective of this research is to conduct a thorough simulation of the Besmaya NGCC power plant under its current operational conditions, thereby establishing a baseline for subsequent analyses. Subsequently, a solar-assisted post-combustion capture (PCC) plant is simulated and seamlessly integrated into the existing power infrastructure. To accurately estimate solar thermal power potential at the Baghdad coordinates, the System Advisor Model (SAM) is employed. The integration of solar thermal energy into the NGCC power plant is meticulously examined, and the resulting hybrid system’s technical viability and performance metrics are rigorously evaluated. The paper contributes to the field by providing valuable insights into the technical feasibility and potential benefits of incorporating solar thermal energy into conventional natural gas power generation infrastructure, particularly in the context of the Besmaya NGCC plant in Baghdad. The power generation capacity of the plant was set at 750 MW. With this capacity, the annual CO2 generation was estimated at 2,119,318 tonnes/year which was reduced to 18,064 tonnes/year (a 99% reduction). The findings aim to inform future decisions in the pursuit of sustainable and efficient energy solutions, addressing both environmental concerns and energy security in the region.

Descriptive feedback with targeted education to improve telephonic escalation of care: a simulation-based study

BackgroundAwareness of communication failures in healthcare has necessitated the implementation of standardized, validated handover tools such as Identification, Situation, Background, Assessment, Recommendation (ISBAR). Although educational sessions improve communication, the effectiveness of individualized care escalation communication training is unknown. The primary aim was to conduct a simulation-based study to assess individualized one-on-one communication training for junior medical doctors for improving care escalation in pediatric emergencies. The secondary aim was to assess the evaluation of the training.MethodsThe prospective observational study assessed participants pre- and post-intervention. In Session One, participants presented a written case scenario telephonically to two senior pediatricians. Fifty participants were scored using an 18-item checklist based on the ISBAR tool and “free text” responses. Immediately following case presentations, participants completed individualized one-on-one 30-minute educational sessions regarding self-reflection, didactic teaching, and constructive feedback based on the ISBAR. Session Two included a second case presentation and reassessment. We conducted qualitative analysis of supervisor’s feedback on performance and trainee doctor’s evaluation of the training.ResultsThere was significant improvement in 8 of the 18 components of the ISBAR checklist. All elements of care escalation were significantly improved, and overall communication was higher post-intervention (P < 0.001); however, no improvement was noted in participants’ explorations of differential diagnoses (P = 0.263). The qualitative analysis identified themes of improved urgency in seeking senior support and conversational clarity from supervisors, and improved intervention quality and self-confidence from participants.ConclusionsIndividualized communication training may improve pediatric emergency care escalation and communication among junior doctors.

Simulation Study on 3D Heterogeneous Photonic Integration with Vertical Microring Coupler

We present a simulation-based study on a 3D heterogeneous photonic integration scheme based on a vertical microring coupler (VμRC). Our research introduces a more compact and efficient layout of photonic devices in the vertical direction, surpassing the limitations of traditional planar integration methods. This investigation focuses on optimizing the performance of the VμRC by analyzing critical parameters such as the dimensions of the microring and the waveguide and the refractive indices of surrounding materials, which serve as the guideline for future manufacturing of the device. The simulation results demonstrate that the careful selection and optimization of these parameters significantly impact the transmittance and coupling characteristics of the VμRC. To demonstrate the validity of this simulation model, we applied it to a few practical cases and achieved comparable results with our previous experiments.

Assessing the Energy Performance and Retrofit Potential of the 1980–1990s’ Residential Building Stock in China’s Jiangsu Province: A Simulation-Based Study

The building operation sector in China represents 22% of the national energy consumption and 22% of the carbon emission, of which urban residential buildings accounted for 24% in 2019. Such figures for the housing sector are projected to increase sharply in the near future, while China aims to peak CO2 emissions by 2030 and reach neutrality before 2060. To reduce the impacts of the urban housing sector and address the energy use and waste generated by large-scale demolition and reconstruction, the central government started promoting the energy retrofit of urban residential buildings, raising such policies to the national strategic level. Jiangsu Province is one of the most urbanised, with a rapid growth in the energy consumption of residential buildings. The Multi-Danyuan and Single-Danyuan Apartment built in 1980–1999 are the most representative residential types in its urban areas. While still adequate functionally, they were designed and built to low energy standards and show significant potential for energy retrofit. Nonetheless, their current performance and energy-saving potential are under-researched, while more detailed and reliable data would be critical to support retrofit design and policy making. This study investigates and characterises the typical use and energy performance of the two building types. Additionally, seven measures and six retrofit scenarios were identified based on the optimal energy reductions and regulations from selected countries. The simulations indicate that, without intervention, the energy consumption of the typical urban residential buildings can reach 122 kWh/m2 under the typical high-energy user scenario. By selecting a set of effective energy-saving measures, the operational energy use for heating and cooling can be reduced by up to 52.4%. Current local standards prove cost-efficient, although less effective in reducing energy use compared to international best practices, indicating potential improvements to the contribution of building retrofit towards achieving the national carbon reduction goals.

Simulation Study of Electromagnetic Standing Wave Method for Pre-stress Detection of Steel Strands

With the widespread application of pre-stressed technology, effective pre-stress monitoring has become a key research area in the field of health monitoring, such as bridges. This paper conducts a simulation-based validation study based on the electromagnetic standing wave pre-stress measurement method. Through the analysis of the implementation process and principles of the electromagnetic standing wave measurement method, frequency domain simulations are performed in HFSS software, and then the frequency domain results are imported into ADS for time domain simulations, ultimately verifying the feasibility of the electromagnetic standing wave pre-stress measurement method. Experimental results indicate that the electromagnetic standing wave method can achieve non-destructive and effective monitoring of in-service pre-stressed steel strands.

Can single-use versus standard duodenoscope improve ergonomics in ERCP? A comparative, simulation-based pilot study.

Background and study aims Musculoskeletal disorders (MSDs) and injuries (MSIs) are frequent in gastrointestinal endoscopy. The aim of this study was to assess potential ergonomic advantages of a lighter single-use duodenoscope compared with a standard reusable one for endoscopists performing endoscopic retrograde cholangiopancreatography (ERCP). Methods Three experienced endoscopists performed an ergonomic, preclinical, comparative protocol-guided simulation study of a single-use and a standard reusable duodenoscope using an anatomic bench model. Surface EMG signals from left forearm and arm muscles were recorded. A commercial inertial sensor-based motion capture system was applied to record body posture as well. Results A significant lowering of root mean square amplitude and amplitude distribution of biceps brachii signal (ranging from 13% to 42%) was recorded in all the participants when using a single-use duodenoscope compared with a reusable one. An overall reduction of muscle activation amplitude and duration was also associated with the single-use duodenoscope for forearm muscles, with different behaviors among subjects. Participants spent most of the time in wrist extension (> 80%) and ulnar deviation (> 65%). A consistent pattern of functional range of motion employed for completing all procedures was observed. Conclusions Our study showed that a lighter scope has a promising effect in reducing upper arm muscle activity during ERCP with potential benefit on musculoskeletal health in the ERCP setting.

Cooling and instabilities in colliding radiative flows with toroidal magnetic fields

ABSTRACT We report on the results of a simulation-based study of colliding magnetized plasma flows. Our set-up mimics pulsed power laboratory astrophysical experiments but, with an appropriate frame change, is relevant to astrophysical jets with internal velocity variations. We track the evolution of the interaction region where the two flows collide. Cooling via radiative losses is included in the calculation. We systematically vary plasma beta (βm) in the flows, the strength of the cooling (Λ0), and the exponent (α) of temperature dependence of the cooling function. We find that for strong magnetic fields a counter-propagating jet called a ‘spine’ is driven by pressure from shocked toroidal fields. The spines eventually become unstable and break apart. We demonstrate how formation and evolution of the spines depend on initial flow parameters and provide a simple analytical model that captures the basic features of the flow.

Material Selection for Differential Gearbox under Torque Conditions: A Simulation-Based Study

Abstract: This research paper conducts an in-depth exploration into the structural behaviors of three distinct materials—Cast Iron, Copper Alloy, and Magnesium Alloy—within the framework of a differential gearbox under varying torque loads. Employing finite element analysis through ANSYS, the investigation systematically scrutinizes critical parameters such as total deformations, equivalent stress (von Mises stress), and shear stress. The primary aim is to offer insights that guide material selection strategies and enhance the optimization of design processes in automotive applications. The results illuminate nuanced material responses when exposed to torque conditions, presenting implications for real-world gear assembly scenarios. This study significantly contributes to the evolving knowledge base in automotive engineering, providing a foundation for wellinformed decision-making in material selection and design optimization for differential gearboxes.

A MATLAB simulation-based analytical study of energy, exergy, and cost benefits in jet-impinged protrusion-roughened double pass solar air collector.

This study analyzes the performance and cost-effectiveness of a protrusion-roughened jet-impinged double-pass solar air collector (PRJDPSAC) within a Reynolds number (Re) range of 2500 to 22,500. Examining jet slot parameters, i.e., the jet height ratio (Hjp/Dhd = 0.11-0.44), stream-wise pitch ratio (Xjp/Dhd = 0.44-1.32), and span-wise pitch ratio (Yjp/Dhd = 0.44-1.32), the model demonstrates enhanced energy conversion, minimizes losses, improves efficiency, and brings positive economic impact, making it a promising solution for diverse applications including drying processes, livestock facilities, remote accommodations, and HVAC system pre-heating. The examination incorporates advanced MATLAB simulations to assess energy-exergy performance and cost viability. At lower Re values, both energy ([Formula: see text]) and exergy ([Formula: see text]) efficiencies increase uniformly; however, stabilization and decline occur at higher Re values. The maximum [Formula: see text] for the PRJDPSAC is 4.38% under a temperature rise parameter of 60 × 10-3 Km2/W for obtaining optimum values of Xjp/Dhd = 1.32, Hjp/Dhd = 0.22, and Yjp/Dhd = 1.32, which is 31% higher than that of the smooth double-pass solar air collector (DPSAC). Economic benefits are significant for PRJDPSAC within mair (0.01-0.07kg/s), but above 0.07kg/s, the DPSAC becomes more cost-effective. Integrating simulation and experimental data, the study highlights MATLAB's effectiveness for solar energy system analysis and optimization, reinforcing the practicality of the proposed collector design.

Development of novel optical character recognition system to reduce recording time for vital signs and prescriptions: A simulation-based study.

Digital advancements can reduce the burden of recording clinical information. This intra-subject experimental study compared the time and error rates for recording vital signs and prescriptions between an optical character reader (OCR) and manual typing. This study was conducted at three community hospitals and two fire departments in Japan. Thirty-eight volunteers (15 paramedics, 10 nurses, and 13 physicians) participated in the study. We prepared six sample pictures: three ambulance monitors for vital signs (normal, abnormal, and shock) and three pharmacy notebooks that provided prescriptions (two, four, or six medications). The participants recorded the data for each picture using an OCR or by manually typing on a smartphone. The outcomes were recording time and error rate defined as the number of characters with omissions or misrecognitions/misspellings of the total number of characters. Data were analyzed using paired Wilcoxon signed-rank sum and McNemar's tests. The recording times for vital signs were similar between groups (normal state, 21 s [interquartile range (IQR), 17-26 s] for OCR vs. 23 s [IQR, 18-31 s] for manual typing). In contrast, prescription recording was faster with the OCR (e.g., six-medication list, 18 s [IQR, 14-21 s] for OCR vs. 144 s [IQR, 112-187 s] for manual typing). The OCR had fewer errors than manual typing for both vital signs and prescriptions (0/1056 [0%] vs. 14/1056 [1.32%]; p<0.001 and 30/4814 [0.62%] vs. 53/4814 [1.10%], respectively). In conclusion, the developed OCR reduced the recording time for prescriptions but not vital signs. The OCR showed lower error rates than manual typing for both vital signs and prescription data.

A case-control study of the infection risk of H5N8 highly pathogenic avian influenza in Japan during the winter of 2020–2021

In Japan, outbreaks of H5N8 highly pathogenic avian influenza (HPAI) were reported between November 2020 and March 2021 in 52 poultry farms. Understanding HPAI epidemiology would help poultry industries improve their awareness of the disease and enhance the immediate implementation of biosecurity measures. This study was a simulation-based matched case-control study to elucidate the risk factors associated with HPAI outbreaks in chicken farms in Japan. Data were collected from 42 HPAI-affected farms and 463 control farms that were within a 5-km radius of each case farm but remained uninfected. When infected farms were detected as clusters, one farm was randomly selected from each cluster, considering the possibility that the cluster was formed by farm-to-farm transmission within an epidemic area. For each case farm, up to three control farms were selected within a 5-km radius. Overall, 26 case farms (16 layer and 10 broiler farms) and 75 control farms (45 layer and 30 broiler farms) were resampled 1000 times for the conditional logistic regression model with explanatory variables comprising geographical factors and farm flock size. A larger flock size and shorter distance to water bodies from the farm were found to increase infection risk in layer farms. Similarly, in broiler farms, a shorter distance to water bodies increased infection risk. On larger farms, frequent access of farm staff and instrument carriages to premises could lead to increased infection risk. Waterfowl visiting water bodies around farms may also be associated with infection risk.