Simulation Model Research Articles

A study on Modeling and Simulation of Sports Training Injury Optimization from a Biomechanical Perspective

With the development of artificial intelligence technology today, sports training is one of the important ways to keep people healthy. Reasonable sports training can keep people happy, and can also improve the physical quality of athletes. However, because the intensity of sports training is greater than the physical endurance, it may lead to physical injury. The sports training injury model can collect the physical data of athletes, and simulate whether the actions that athletes want to practice are standard and correct through the collected data. If there are errors in the simulated exercise actions, the wrong exercise actions can be corrected in time, so as to reduce the incidence of sports injury accidents. The construction of the experimental model and the calculation and analysis process of the construction data are very complex, and the existing simulation models are difficult to achieve this, which has a significant impact on the subsequent development of the simulation model. In view of this phenomenon, this paper, on the basis of the sports training injury model, combined with the neural network method, conducted an effective research on the construction of the sports training injury model, and inspected the performance of the model by testing the accuracy of the evaluation of the injury risk level, the degree of sports training injury, and the accuracy of the evaluation of the prediction results of the sports training injury model. The experimental data shows that the maximum accuracy of the sports training injury model in the horizontal and vertical directions was 94.43% and 95.26% respectively, and the maximum accuracy of the single injury degree and the composite injury degree was 48.68% and 55.01% respectively, which had high accuracy and operational efficiency, and can effectively avoid the occurrence of training injury.

Forecasting Financial Expenditures for Long Term Care Insurance Evidence from the Typical Pilot Cities in C

Purpose: This study aims to provide decision-makers with a reference by predicting the financial expenditures and trends of long-term care insurance in China over the next thirty years. Approach/Methodology/Design: This study employs Markov chains and overall simulation model to forecast the extent of financial expenditures on long-term care for employees and residents. It examines the financial outlay levels under various entitlement payment scenarios and estimates the discrepancies in financial expenditures between employees and residents from 2021 to 2050. Findings: The results show that (1) The number of disabled elderly employees aged 60 and above will be about 14,288 thousand, and the number of disabled elderly residents will be about 68,176 thousand in 2050. (2) Under the low scenario, the financial expenditures of elderly employees and residents in 2050 will reach 29,636.87 million yuan and 17,597.92 million yuan respectively, which are 2.86 and 10.24 times as much as those in 2021. The total financial expenditures under the medium and high scenarios will be 2.38 and 5.51 times as much as those under the low scenario respectively. (3) The scale of financial expenditure on long-term care insurance for residents far exceeds that of employees, and the trend is expanding. Practical Implications: This study may provide theoretical support for the full implementation of long-term care insurance policies. Originality/value: This study uses Markov models and overall simulation models, along with typical representative plans from pilot areas in China, to differentiate the insurance identity of elderly employees and residents and to estimate the scale and trend of financial expenditures for long-term care insurance in China from 2021 to 2050.

Simulation and Optimization of Automated Guided Vehicle Charging Strategy for U-Shaped Automated Container Terminal Based on Improved Proximal Policy Optimization

As the decarbonization strategies of automated container terminals (ACTs) continue to advance, electrically powered Battery-Automated Guided Vehicles (B-AGVs) are being widely adopted in ACTs. The U-shaped ACT, as a novel layout, faces higher AGV energy consumption due to its deep yard characteristics. A key issue is how to adopt charging strategies suited to varying conditions to reduce the operational capacity loss caused by charging. This paper proposes a simulation-based optimization method for AGV charging strategies in U-shaped ACTs based on an improved Proximal Policy Optimization (PPO) algorithm. Firstly, Gated Recurrent Unit (GRU) structures are incorporated into the PPO to capture temporal correlations in state information. To effectively limit policy update magnitudes in the PPO, we improve the clipping function. Secondly, a simulation model is established by mimicking the operational process of the U-shaped ACTs. Lastly, iterative training of the proposed method is conducted based on the simulation model. The experimental results indicate that the proposed method converges faster than standard PPO and Deep Q-network (DQN). When comparing the proposed method-based charging threshold with a fixed charging threshold strategy across six different scenarios with varying charging rates, the proposed charging strategy demonstrates better adaptability to terminal condition variations in two-thirds of the scenarios.

Hybrid Simulation Model of Lifecycle Simulation and Replacement Simulation Considering Carbon Lock-In by Coal-Fired Power Plants

To meet the temperature goal set by the Paris Agreement, cumulative CO2 emissions must be kept below 300 GtCO2. Road transportation accounted for approximately 18% of the CO2 emissions from fuel combustion in 2017. Electric vehicles (EVs) have been rapidly adopted by environmentally conscious consumers in many countries to reduce CO2 emissions. EVs have lower emission intensities than do internal combustion engine vehicles (ICEVs) in most parts of the world, except where the penetration of renewable energy is low in the energy production mix. In such places, the CO2 emissions of EVs are larger than those of ICEVs. Despite the obvious need to increase renewable energy sources to reduce CO2 emissions, Japan and many other countries around the world have yet to shift away from fossil fuels. This is due in part to carbon lock-in, which refers to prior decisions related to technologies and infrastructure that constrain the implementation of better paths toward low-carbon technologies. Coal-fired power plants are the most problematic in terms of carbon lock-in because of their high carbon intensities and long physical lives. In addition, because carbon lock-in by coal-fired power plants has a significant impact on the embodied CO2 intensity of grid power, it impacts society through products that use electric power. In this study, we propose a hybrid simulation model of lifecycle simulation and replacement simulation, considering carbon lock-in by coal-fired power plants. In the replacement simulation, we simulated the replacement of end-of-life coal- and oil-fired power plants with renewable energy power plants using a probability called the lock-in rate and estimated the changes in the embodied CO2 intensity of grid power in Japan. In the lifecycle simulation, we evaluated cumulative CO2 emissions from entire product lifecycles of ICEVs and EVs based on three different EV diffusion scenarios. The results showed that the lock-in rate of coal-fired power plants strongly affects the decarbonization effect due to the market diffusion of EVs.

Computer simulation of the truck cabin tests to find ways to meet the requirements of the UNECE Regulation 29

BACKGROUND: Ensuring the required level of passive safety of trucks is a relevant task. To solve this problem, it is necessary to use computer simulation methods. AIM: Search for the ways to ensure the passive safety of trucks by choosing the appropriate cabin design. MATERIALS AND METHODS: During the study, the method of virtual simulation of the truck cabin tests was used in accordance with the requirements of the UNECE Regulation 29. RESULTS: A simulation model of a truck cabin has been developed, which makes it possible to conduct the computer simulation of the tests according to the UNECE Regulation 29. The main requirement of the UNECE Regulation 29 is retaining sufficient space in the cabin to accommodate the driver’s dummy inside it after its testing. As a part of the study, the properties of the material (steel) used for manufacturing of prototype cabins of the truck under development were determined, and the tested specimens were obtained by dissecting the prototype of the cabin, so the obtained results take into account the technological history of the material. The obtained data on the properties of the materials were used in the development of the simulation model of the cabin. The developed and used simulation model takes into account all the design features of the cabin under study, including the location and properties of welding points. Various options of cabin design changes and their impact on the ability to meet the requirements of the UNECE Regulations 29 are investigated. It has been found that it is not possible to fully comply with the requirements of the UNECE Regulation 29 only by increasing (within reasonable limits) the thickness of individual elements of the cabin frame and/or using high-strength steel for their manufacturing. Based on the analysis of the deformation of the prototype cabin frame during the Test C according to the UNECE Regulation 29 Revision 2, proposals for changes to the cabin design to meet the requirement of the UNECE Regulation 29 are formulated. CONCLUSION: The results of the study indicate the prospects of the proposed changes to the cabin design.

Design of a Simulation Model for the Diagnosis of Classical Swine Fever Virus in Ecuadorian Farms

Classical swine fever (CSF) is a disease that slows down animal production and international trade; therefore, its identification is key in pig farms to take the relevant health measures. Therefore, the objective of this research was to design a Susceptible-Exposed-Infected-Recovered (SEIR) simulation model to carry out epidemiological modeling for the identification of outbreaks of classical swine fever in the Sierra Region of Ecuador, using Python software and historical data on incidences of this disease in the provinces of the Ecuadorian highlands, considering the variables pig population, initial number of exposed pigs, initial number of infected, number of pigs removed, contagion rate (α), transmission rate (β), and recovery rate (γ). The results show that the SEIR model allowed us to determine that the population of susceptible (healthy) pigs decreases over time until reaching zero. This decrease in susceptibility occurred during the first 15 days, which shows that this is the time necessary to infect the entire population with an infected person. Therefore, the exposed population increases during the 15 days that the total infection process lasts and then decreases. It is also identified that throughout these five years of analysis of the past, it has been increasing from 2015 to 2019, which hurt the yields and productivity of pig farms in the Ecuadorian mountains.

A novel low-cost high-fidelity porcine model of liver metastases for simulation training in robotic parenchyma-preserving liver resection.

In the era of minimally invasive surgery (MIS), parenchyma-preserving liver resections are gaining prominence with the potential to offer improved perioperative outcomes without compromising oncological safety. The surgeon learning curve remains challenging, and simulation plays a key role in surgical training. Existing simulation models can be limited by suboptimal fidelity and high cost. We describe a novel, reproducible, high-fidelity, low-cost liver metastases model using porcine livers from adult Landrace pigs, with porcine perinephric fat used to simulate subcapsular metastases. This model was then utilised in a training session for surgical trainees performing robotic parenchyma-preserving surgery (PPS) under the guidance of expert robotic surgeons, with feedback being recorded. Trainees rated the model highly on its fidelity to human liver simulation (median score 9), tissue handling (median score 8), and overall usefulness (median score 9). Tissue handling was felt to simulate in vivo liver resection closely, while suggestions for improvement included adding simulated blood flow. This is a novel, low-cost, high-fidelity simulation model of liver metastases with high acceptability to surgical trainees, which could be readily adopted by other training centres.

Unraveling Uncertainty: The Impact of Biological and Analytical Variation on the Prediction Uncertainty of Categorical Prediction Models.

Interest in prediction models, including machine learning (ML) models, based on laboratory data has increased tremendously. Uncertainty in laboratory measurements and predictions based on such data are inherently intertwined. This study developed a framework for assessing the impact of biological and analytical variation on the prediction uncertainty of categorical prediction models. Practical application was demonstrated for the prediction of renal function loss (Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI] equation) and 31-day mortality (advanced ML model) in 6360 emergency department patients. Model outcome was calculated in 100 000 simulations of variation in laboratory parameters. Subsequently, the percentage of discordant predictions was calculated with the original prediction as reference. Simulations were repeated assuming increasing levels of analytical variation. For the ML model, area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity were 0.90, 0.44, and 0.96, respectively. At base analytical variation, the median [2.5th-97.5th percentiles] percentage of discordant predictions was 0% [0%-28.8%]. In addition, 7.2% of patients had >5% discordant predictions. At 6× base analytical variation, the median [2.5th-97.5th percentiles] percentage of discordant predictions was 0% [0%-38.8%]. In addition, 11.7% of patients had >5% discordant predictions. However, the impact of analytical variation was limited compared with biological variation. AUROC, sensitivity, and specificity were not affected by variation in laboratory parameters. The impact of biological and analytical variation on the prediction uncertainty of categorical prediction models, including ML models, can be estimated by the occurrence of discordant predictions in a simulation model. Nevertheless, discordant predictions at the individual level do not necessarily affect model performance at the population level.

Community-based reconstruction and simulation of a full-scale model of the rat hippocampus CA1 region.

The CA1 region of the hippocampus is one of the most studied regions of the rodent brain, thought to play an important role in cognitive functions such as memory and spatial navigation. Despite a wealth of experimental data on its structure and function, it has been challenging to integrate information obtained from diverse experimental approaches. To address this challenge, we present a community-based, full-scale in silico model of the rat CA1 that integrates a broad range of experimental data, from synapse to network, including the reconstruction of its principal afferents, the Schaffer collaterals, and a model of the effects that acetylcholine has on the system. We tested and validated each model component and the final network model, and made input data, assumptions, and strategies explicit and transparent. The unique flexibility of the model allows scientists to potentially address a range of scientific questions. In this article, we describe the methods used to set up simulations to reproduce in vitro and in vivo experiments. Among several applications in the article, we focus on theta rhythm, a prominent hippocampal oscillation associated with various behavioral correlates and use our computer model to reproduce experimental findings. Finally, we make data, code, and model available through the hippocampushub.eu portal, which also provides an extensive set of analyses of the model and a user-friendly interface to facilitate adoption and usage. This community-based model represents a valuable tool for integrating diverse experimental data and provides a foundation for further research into the complex workings of the hippocampal CA1 region.

Asymmetric ENSO teleconnections in a symmetric CO2 concentration pathway

Abstract El Niño-Southern Oscillation (ENSO) is the strongest interannual phenomenon occurring in the tropical Pacific, significantly affecting the entire world. The ENSO response to increasing CO2 concentrations have been extensively studied, but the reverse scenario is still not well understood. Here, we investigate the hysteresis of ENSO teleconnections in a CO2 removal simulation of an earth system model. During the ramp-up period of CO2 concentrations, Pacific-North American and Pacific-South American patterns are intensified, with the eastward shift of their poles, which are even further intensified during the ramp-down period. This ENSO teleconnection hysteresis is closely linked to the tropical-origin hysteresis, in which, during the ramp-down periods, the prevalence of the eastern-Pacific type El Niño leads to the hysteresis in the eastern Hemispheric ENSO teleconnections and the enhanced ENSO skewness and the eastward shift of ENSO-induced tropical atmospheric convection do to that in the western Hemispheric ENSO teleconnection. The alterations by the tropical origin are predominantly associated with intensified southward migration of the Intertropical Convergence Zone, along with a stronger El Niño-like warming trend. We also demonstrate that the hysteretic change in the mid-latitude mean state over the North Pacific region could lead to hysteresis of the ENSO teleconnection without invoking a tropical origin.

Fast-Time Simulations to Study the Capacity of a Traffic Network Aimed at Urban Air Mobility

This article investigates viable solutions to implement an Urban Air Mobility network in Milan, Italy, and analyzes its influence on the airspace capacity. The network comprises eight vertiports for passenger transport among two main airports in the area and the city using electric vertical take-off and landing aircraft (eVTOLs). A Fast-Time Simulation (FTS) model with the software AirTOp (Air Traffic Optimization) allowed the evaluation of the ideal capacity of the network by varying two configurations, which differ from each other in terms of the number of Final Approach and Takeoff areas (FATOs). The results show how it is possible to reach high hourly capacities (in the order of one hundred), thus allowing the use of the service for about 4% of the total passengers passing through the two airports during the reference day chosen for this study. However, the results are ideal due to the strong idealism of the system, which overlooks several factors, and they should be considered as the maximum limit that can be obtained. Despite this, the method presented in this article can also be adapted for other urban areas with high population densities. In addition, the use of a simulation tool of this type allows, in addition to a numerical analysis, a qualitative analysis of the network behavior in terms of traffic, thus highlighting the criticalities of the proposed systems.

Sacrospinous Hysteropexy-Video Tutorial of Construction and Application of a Feasible and Inexpensive Teaching Model for Simulation.

Sacrospinous hysteropexy is one of the preeminent uterus-preserving surgical techniques for treating pelvic organ prolapse supported by level one evidence. As training on models greatly improves surgical skills and outcomes, we developed a simple and inexpensive model to simulate sacrospinous hysteropexy. A step-by-step instruction for the production of the model is available to be viewed online. To keep production costs low, readily available materials were used, with a total cost per model of about 2 EUR (Austria, August 2023). All important anatomical landmarks (prolapsing uterus, vagina, ischial spine and sacrospinous ligament) were all represented. We present a detailed instructional video on how to construct the model and the practical training, detailing the individual steps of a successful sacrospinous hysteropexy, available online. Thus, trainees are able to practice the individual movements of the entire surgical procedure on this simulator model guided by the tutorial video. In this way, trainees will be able to practice the entire surgical procedure. An introduction to the model with explanation of all anatomical landmarks and a standardised explanation of the surgery with its individual steps (handout distributed). The presented video showcases the feasibility of the easy construction and application of a model for the surgical skill training of sacrospinous hysteropexy. Easily accessible, inexpensive material and its simple build make this a reproducible model regardless of geographic or socioeconomic resources.

Predictive Model of the Effects of Skin Phototype and Body Mass Index on Photobiomodulation Therapy for Orofacial Disorders

Monte Carlo techniques have been extensively used for planning laser-based clinical procedures such as photobiomodulation. However, the effects of several biological tissue characteristics regarding its morphological structure and physiological parameters have not been carefully addressed in many applications. Specifically, many questions remain concerning the effect of skin phototype and body mass index on the effectiveness of photobiomodulation for extraoral therapies. To address these questions, a Monte Carlo simulation model of the effects of body mass index-dependent skin structure on different Fitzpatrick skin types was developed, specifically tailored for the morphological characteristics of cheek tissue. The model describes the settings of a typical oral photobiomodulation treatment protocol for pain relief, namely the use of 660 nm and 808 nm laser wavelengths and a therapeutic dose of 2.0J/cm2 on the masseter muscle. The simulations were used to train a machine learning predictive model aimed at accelerating the treatment planning stage and assessing the importance of patient-specific parameters. A multiple-regression approach was adopted to predict muscle dose and treatment time for effective delivered dose. Body mass index had little effect on epidermal energy deposition, but an important impact on muscle dose parameters. Phototype also influenced muscle dose, but to a lesser extent than body mass index. The results of this study can be used to develop customized dosimetry phototherapy protocols to promote more effective and safe clinical outcomes.

Resolving the conflict between the need for electricity generation upstream and the water supply for economic development downstream in the Ca River Basin, Vietnam

ABSTRACT Vietnam has vigorously developed hydroelectric reservoirs in main river basins with hydroelectricity reserves to produce electricity and exploit and use water resources efficiently and minimally – risks to meet socio-economic development requirements – in the downstream area. The study has shown evidence that the water level's daily mean value of the Ca River is more and more reduced. However, the number of occurrences of the daily minimum flow was more than that of the period when it was not hydropower reservoirs and was affected by climate change. The study calculated and built five scenarios for water use in the downstream areas. These scenarios are calculated based on the water demand for domestic, industry, irrigation, and water sea level. A simulation model for optimal hydropower generation of reservoir system upstream and forced to discharge downstream with five scenarios. This study's results demonstrated that four upstream reservoirs could operate with scenario 3 corresponding HNam Dan = 0.7 m. The results suggested options for the operation of hydroelectric reservoirs and simultaneously developing scenarios for taking water for agricultural irrigation downstream of the Ca River Basin with water level for irrigation drains and domestic water factories. The benefit of electricity in four reservoirs in scenario 3 reached 1,009 billion VND.

Cost-Effectiveness of Temporary Financial Assistance for Veterans Experiencing Housing Instability.

The US Department of Veterans Affairs (VA) partners with community organizations (grantees) across the US to provide temporary financial assistance (TFA) to vulnerable veterans through the Supportive Services for Veteran Families (SSVF) program. The goal of TFA for housing-related expenses is to prevent homelessness or to quickly house those who have become homeless. To assess the cost-effectiveness of the SSVF program with TFA vs without TFA as an intervention for veterans who are experiencing housing insecurity. This study used a Markov simulation model to compare cost and housing outcomes in a hypothetical cohort of veterans enrolled in the SSVF program. Enrollees who are homeless receive rapid rehousing services, while those who are at risk of becoming homeless receive homelessness prevention services. The SSVF program with TFA for veterans who are experiencing housing insecurity. The effectiveness measure was the incremental cost-effectiveness ratio (ICER) with quality-adjusted life-years (QALYs). The model was parameterized using a combination of inputs taken from published literature and internal VA data. The model had a 2-year time horizon and a 1-day cycle length. In addition, probabilistic sensitivity analyses were conducted using 10 000 Monte Carlo simulations. The base case analyses found that the SSVF program with TFA was more costly ($35 814 vs $32 562) and yielded more QALYs (1.541 vs 1.398) than the SSVF program without TFA. The resulting ICER was $22 676 per QALY, indicating that TFA is the preferred strategy at a willingness-to-pay threshold of $150 000 per QALY. This ICER was $19 114 per QALY for veterans in the rapid rehousing component of the SSVF program and $29 751 per QALY for those in the homelessness prevention component of the SSVF program. At a willingness-to-pay threshold of $150 000 per QALY, probabilistic sensitivity analyses showed that TFA was cost-effective in 8972 of the 10 000 Monte Carlo simulations (89.7%) for rapid rehousing and in 8796 of the 10 000 Monte Carlo simulations (88.0%) for homelessness prevention only. This economic evaluation suggests that TFA is a cost-effective approach (ie, yields improved health benefits at a reasonable cost) for addressing housing insecurity for veterans enrolling in the SSVF program. Future research could examine the cost effectiveness of large, nationwide housing interventions such as this one among subpopulations of veterans such as those with certain comorbidities including severe mental illness or substance use disorders, those with chronic diseases, or those experiencing long-term housing instability vs acute loss of housing.

Research on the Propagation Law of Fire Smoke on the Working Face of a Belt Conveyor

In view of the spread and distribution of high-temperature toxic smoke on the working face during belt conveyor fires, the FDS was used to carry out numerical simulation, establish a belt conveyor fire simulation model, set up a variety of working conditions, and study the flue gas spread of the working face with different ignition source locations and different heat release rates. The results show that the flue gas reaching the working face varies greatly from different ignition source locations, and the smoke propagation time of the working face decreases first and then increases with the increase in the scale of the fire. The location of the fire source is from 0 to 700 m, and the visibility of the working face will drop to less than 3 m within 10 min, which seriously affects emergency evacuation; the maximum concentration of CO in the working face is proportional to the heat release rate, the fire source is less than 100 m away from the working face, and the temperature of the air inlet area of the working face is higher than 60 °C, which poses a great threat to personnel evacuation. When the fire source is less than 200 m away from the working face, the evacuees will encounter smoke damage on the working face, and when the fire scale reaches 4 MW and 6 MW, the CO concentration will have a great impact on the evacuation and make people incapacitated.

Rewritable Broadband Lossy Absorber Based on Laser Induction Technology

AbstractThe tunable and reversible fabrication function of stealth metasurfaces has significant application value in complex electromagnetic environments., but the extremely low fault tolerance of the existing fabrication methods limits their further development and utilization. In this manuscript, a design and fabrication method for rewritable broadband stealth metasurfaces with memory function is proposed. The reversible phase transition is achieved by laser induced germanium telluride (GeTe) film, which provides the possibility for metasurface to realize the rewriting function. The process of laser induced GeTe and the simulation model of GeTe are investigated, and the conclusions are verified by rewritable broadband polarization converter (RBPC) and rewritable broadband lossy absorber (RBLA). The experimental results show that the reflectivity of fabricated RBLA is less than −10 dB in the range of 7.8–16.1 GHz, which is in good agreement with the numerical simulation results. Meanwhile, there is a highly consistent performance effect before and after repeated induction. The research has the advantages of high efficiency, region selectivity, non volatility and high fault tolerance, which can provide new manufacturing ideas and good candidates for tunable metamaterials.

Energy Storage Solutions to De-Carbonize the Electric Supply in Jordan

This paper aims to estimate the size of Energy Storage Systems (ESS) required de-carbonizing the electrical network in Jordan. Load profile in addition to the PV and Wind energy profiles were studied and used as input data to the simulation model. Different cases of RE penetration levels as a percentage of load demand were considered and entered to the simulation model as well. Other factors, such as allowed over-generation and ESS efficiency also provided and calculations carried out accordingly. In addition to the technical consideration, an economic analysis approach added to the study. Results show that staying with the current level of RE penetrations, i.e. at PV 11% and W 9% (RE level 20% of load demand) and 5% curtailment, no need to install ESS to absorb the RE intermittency and variability. As RE penetration level increases, the need for ESS becomes necessary, i.e. to reach 100% RE penetration and de-carbonize the electrical network accordingly, an ESS of 138,900.00MWH/ 7,386.57MW PHES would be required at PV 65% and Wind 35% penetration level and 25% curtailment with initial CAPEX investment of JOD 15,367,314,444.10. Economic feasibility study shows that with the current RE energy prices and ESS CAPEX, reaching the 100% RE penetration level would not be the optimum solution as there will be a negative cost impact, unless these prices reduced to a certain level making the solution feasible.

Optimizing laser-induced phase transformations in Sb2S3 thin films: Simulation framework and experiments

We present a novel simulation approach combined with pulsed laser experiments, spectroscopic ellipsometry, and Raman spectroscopy to comprehensively analyze phase transformation dynamics in thin films. The simulations apply to any thin film stack and incorporate critical factors, such as thin film interference, heat transfer, and temperature-dependent optical properties during heating and melting. As a case study, we investigate the picosecond laser-induced amorphization of antimony sulfide (Sb2S3) thin films, a promising alternative to traditional phase-change materials in photonic applications to validate the simulation model. The computational efficiency of our simulations enables not only the investigation of the laser-induced phase transformation but also the optimization of key process parameters and parameter fitting. The simulations identified optimal film thickness and laser fluence parameters that maximize energy efficiency, melting effectiveness, and quenching rate while ensuring high reflectivity contrast between the amorphous and crystalline states. By constructing a wide-ranging, high-resolution parameter map of the laser fluence and film thickness dependence of the melting process, we demonstrate how this model guides the understanding of phase transformation dynamics. Raman spectroscopy confirms the polycrystalline to amorphous transition of Sb2S3 and provides a semiquantitative estimate of the amorphous fraction as a function of laser fluence, which is qualitatively consistent with the simulation predictions of the model. The open-source simulation framework, experimentally validated, provides valuable insights into laser-induced amorphization dynamics in Sb2S3 and related phase-change material thin films, enabling rapid optimization of photonic devices.

Investigation on Novel Ultralight Weight Thermally Insulated Fireproof Composite

Abstract This study highlights the performance of ultra-lightweight fireproof composite. Polyacrylonitrile (PAN) based carbon fiber (CF) has been reinforced in Polyetherimide (PEI) polymer to develop the composite. The Surface of CF and PEI film was modified by low-pressure plasma to improve the bonding strength between matrix and reinforcement. The polymeric composite was fabricated by compression moulding with a pressure of 2 bar, temperature of 380 ℃ and holding time of 30 min. CF/PEI composite was used to make a hybrid composite by layering of silicone foam in between the layers. The hybrid composite was exposed to a Bunsen burner under sustained flame for a duration of 10 minutes. The composite panel's flame-facing side reached 676.2°C after 10 minutes of fire exposure, while the temperature on the other side only reached 58.2°C. The fabricated hybrid composite was exposed to very low temperature in order to test its ability of thermal insulation under extreme cold temperature. Over the specific period of testing, the temperature of the dry ice decreased from 25°C to -3.1°C. After exposure to fire, only minimal loss of material was observed. The hybrid composite of carbon fiber and PEK film, sandwiched between silicone foam, exhibits excellent fire resistance due to its high limiting oxygen index (LOI). This composite is considered to be among the best thermally insulating and fire-resistant materials. Thermogravimetric analysis (TGA) of carbon fiber and PEI-Carbon fiber composite was performed to determine the optimal processing temperature of compression moulding for the composite, upon heating, it showed a modest weight decrease of 6.053%. The composite shows a significant improvement of impact resistance, compressive strength and thermal stability. A simulation model was developed under Ansys fluent software for both heating and cooling. The analysis of developed model also shows similar results.