Simulation Work Research Articles

Investigation of the Performance of Plugging Braking System as a Hill Descent Control (HDC) for Electric-Powered Wheelchair

Recently, a study on Electric Powered Wheelchairs (EPWs) has become significant because they can enhance the mobility of individuals with disabilities. One of the issues on EPW is during descending on a slope because it is difficult to control the speed and prevent it from slipping. Moreover, the manual braking system is frequently used for speed control by pressing the brake lever. The complexity of the task increases significantly when dealing with elderly or paralyzed users with physical limitations. Consequently, the risk of collisions and injuries is elevated. This research seeks to develop a hill descent control (HDC) system for an EPW to address these challenges. By implementing HDC into EPW, the EPW's speed can be controlled, thus increasing the safety of the EPW while descending on the slope. In this study, the plugging brake system is introduced as a hill descent control (HDC) mechanism to inhibit the acceleration of the EPW and ensure it maintains a constant speed during downhill descents. The plugging voltage will be controlled based on the desired speed of 0.6 m/s. To maintain the speed of the EPW, the PID control is used as a control strategy for HDC. The simulation work in Matlab Simulink has analyzed the performance of the plugging brake system with HDC. The results obtained from the simulation reveal that, despite starting with a high initial braking speed of 2.5 m/s, the Electric Powered Wheelchair (EPW) can consistently maintain its velocity at the desired target value of v_d = 0.6 m/s during the descent on the slope. Furthermore, the amplitude response for the PID control shows the settling time is 2.3 s, and the steady-state error is ±0.05. Based on the simulation results, it can be approved that the proposed HDC in the plugging brake system can prevent the EPW from accelerating while descending on the slope and improve the safety of the EPW.

A comparison study between the realistic random modeling and simplified porous medium for gamma-gamma well-logging

The accurate determination of formation density and the physical properties of rocks is the most critical logging tasks which can be obtained using gamma-ray transport and detection tools. Though the simulation works published so far have considerably improved the knowledge of the parameters that govern the responses of the detectors in these tools, recent studies have found considerable differences between the results of using a conventional model of a homogeneous mixture of formation and fluid and an inhomogeneous fractured medium. It has increased concerns about the importance of the complexity of the model used for the medium in simulation works. In the present study, we have suggested two various models for the flow of the fluid in porous media and fractured rock to be used for logging purposes. For a typical gamma-gamma logging tool containing a 137Cs source and two NaI detectors, simulated by using the MCNPX code, a simplified porous (SP) model in which the formation is filled with elongated rectangular cubes loaded with either mineral material or oil was investigated. In this model, the oil directly reaches the top of the medium and the connection between the pores is not guaranteed. In the other model, the medium is a large 3-D matrix of 1 cm3 randomly filled cubes. The designed algorithm to fill the matrix sites is so that this realistic random (RR) model provides the continuum growth of oil flow in various disordered directions and, therefore, fulfills the concerns about modeling the rock textures consist of extremely complex pore structures. For an arbitrary set of oil concentrations and various formation materials, the response of the detectors in the logging tool has been considered as a criterion to assess the effect of modeling for the distribution of pores in the formation on simulation studies. The results show that defining a RR model for describing heterogeneities of a porous medium does not effectively improve the prediction of the responses of logging tools. Taking into account the computational cost of the particle transport in the complex geometries in the Monte Carlo method, the SP model can be satisfactory for gamma-gamma logging purposes.

New Crystal Photodiode Combination for Environmental Radiation Measurement

Here, a new design will be introduced to detect radiation in the environment with high efficiency. The designed structure consists of placing ZnS–Si APD and PIN photodiodes at the ends of conventional crystals such as NaI(Tl) and CsI(Tl). Since ZnS is transparent to photons with wavelengths between 340 and 10000 nm, photons coming from the crystals are absorbed directly in the depletion region and generate primary particles. With an increase in the number of generated primary particles, a stronger and cleaner signal is obtained. In the simulation work, the light generated by 30 keV–3 MeV gamma rays in the crystals was obtained using the Geant4 simulation code. The single-particle Monte Carlo technique was used to calculate the photodiode output signal for the crystal emission spectrum. The simulation results showed that the crystals and ZnS–Si photodiode structures formed a good combination. The high quantum efficiency and low excess noise factor make the ZnS–Si structure an excellent choice for scintillating light detection.

A New Design and Simulation of Smart Sensing for Emergency Vehicle

All emergency vehicles are designed and certified to better respond to emergencies. Also, emergency vehicles have the legal right to violate the highway code to get to a certain area as quickly as possible. The victim's existence is helpless in the event of a stroke of fate or an emergency. Because of the visitors, emergency vehicles also have to give way to get to the scene. This change can also further increase the victim's chance of death. Therefore, to solve the problem, it is crucial to plan and limit travel time so that emergency vehicles use their maximum capacity. Preventive service can be a means for emergency vehicles to reach their intended vacation spot in a much shorter time. So, there is a call to address this emergency vehicle action. This look at strategies is an intelligent detection utility to deal with alarming traffic jams. This mission aims to simulate the intelligent detection of emergency vehicles through simulation software. The purpose of e-Buzz: Simulation of Smart Sensing for Emergency Vehicles is to assist the government that may be responsible for responding to emergencies, including ambulance, police, fire, and many others. The main element to ensure that this simulation works is based on the type of sensor used and its usefulness and responsibility. The sensor can detect the sound of emergency vehicles and respond immediately to them by signalling smaller parallel visitors to change the screen from pink to green or vice versa. Therefore, this flow reduces the travel time required for emergencies.

Flow analysis of a control ball valve in a common rail fuel injector

The electronic control common rail fuel injection system is one of the key technologies being used in modern diesel engines to improve the engine performance. A numerical simulation was conducted to study the characteristics of the fuel flow around the ball valve in the common rail fuel injector. The lift of the control ball valve and static pressure of the flow field around the control ball valve were measured respectively. The simulation was performed with the measurements as boundary conditions such that the flow characteristics can be correlated with the valve lift and the pressure upstream of the ball valve. Simulation results showed that during the valve opening and closing, a high pressure area appeared below the ball valve, while cavitation appeared in the area around the control ball valve. Specifically, the cavitation mainly appeared around the sealing circle, and its upstream area and the conical surface from the entrance to a certain distance downstream. The flow throttling happened at the outlet orifice under the steady-state condition when the valve was at the maximum opening position. The cavitation around the control ball valve was more serious under higher rail pressure conditions. The formation and development of the cavitation was found to be depending on the lift position of the ball valve and pressure of the flow around the ball valve during the valve opening and valve closing. Higher value of void fraction appeared in the flow when the lift of the ball valve was at a larger opening position. It was found that cavitation around the ball valve will affect the consistency and stability of the fuel flow. And cavitation erosion found at the valve seat from a simplified durability test appeared to be close to the place where cavitation appeared from simulation. Therefore, it is suggested that simulation work can be used to examine if cavitation occurs in the flow path of the control ball valve inside the injector in design phase and make necessary changes on the design to prevent the cavitation and the resulting damages to the control ball valve in the injector.

Temporal encoding in deep reinforcement learning agents

Neuroscientists have observed both cells in the brain that fire at specific points in time, known as “time cells”, and cells whose activity steadily increases or decreases over time, known as “ramping cells”. It is speculated that time and ramping cells support temporal computations in the brain and carry mnemonic information. However, due to the limitations in animal experiments, it is difficult to determine how these cells really contribute to behavior. Here, we show that time cells and ramping cells naturally emerge in the recurrent neural networks of deep reinforcement learning models performing simulated interval timing and working memory tasks, which have learned to estimate expected rewards in the future. We show that these cells do indeed carry information about time and items stored in working memory, but they contribute to behavior in large part by providing a dynamic representation on which policy can be computed. Moreover, the information that they do carry depends on both the task demands and the variables provided to the models. Our results suggest that time cells and ramping cells could contribute to temporal and mnemonic calculations, but the way in which they do so may be complex and unintuitive to human observers.

Design and Improvement of Dewatering System Implemented for Biogas Slurry

The project addresses the pressing need for organic fertilizers in Nepal and explores an eco-friendly alternative to chemical fertilizers. By dewatering biogas residue, this research contributes to sustainable agriculture practices. The project's methodology involved a combination of research, literature recommendations, and guidance from experienced supervisors. SolidWorks was utilized for the design phase, while ANSYS facilitated simulation works. Subsequently, the testing of the dewatering machine was conducted. The biogas slurry used for testing and sun-drying was sourced from a prototype 1m3 capacity biogas plant. The machine's performance was evaluated at various operating speeds, with 8 rpm identified as the most efficient. Notable outcomes included a liquid yield of 52.51%, an extraction efficiency of 79.11%, and extraction losses amounting to 22.80%. To enhance the quality of the organic fertilizer, the slurry sample underwent sun-drying for different durations: 24, 48, and 72 hours. Nutrient concentrations (TS, N, P, K) were analyzed, that came with the 48-hour sun-drying period proving to be the most effective in reducing Total Solid contents. Comparatively, pre-treating the slurry sample for 24 and 48 hours prior to testing in the dewatering machine allowed for an assessment of the machine's efficiency, offering valuable insights to be contrasted with the sun-drying process. This research paves the way for a greener, more efficient method of dewatering biogas slurry, aligning with Nepal's agricultural and environmental sustainability goals.

Coarse-grained molecular dynamics study on submicron structuring of calcium silicate hydrate with enhanced tensile modulus and strength

Potential of cement-based materials for energy harvesting is encoded in their elusive mesoporous structure, and understanding the sub-micron structure of calcium silicate hydrate (C–S–H), a major binding phase of cement, is an important yet challenging task. Nanoscale C–S–H is ductile, while macroscopic performance is brittle, suggesting a gap in the transition of material properties from the nano-to the macro-scale, and a chance in finetuning the submicron (∼100 nm) structure to effectively deliver the atomistic properties to the bulk performance. The refined sub-micron structure of C–S–H can enhance the strength and modulus of concrete, thus providing a base material with better mechanical properties and durability. Here, two critical structural features of C–S–H, including agglomeration of globules and stacking of layers, are modeled by spherical and oblate ellipsoidal units, respectively. Coarse-grained modeling and peridynamics simulations find that the packing of ellipsoidal units is better than that of spherical units, from the perspectives of size distribution, morphology, fractality of pores and tensile properties. The analyses on the mesoporous structure shows that the ellipsoid-packing is more homogeneous than the sphere-packing model, and it yields a refined structure with enhancement in tensile modulus and strength. The submicron finetuning enhancement of C–S–H can be realized through different physical and chemical approaches, like inducing lateral growth of silicate chains by chemical additives, scaffolding materials, and conditioned hydration. This coarse-grained simulation work gives novel insights and fundamental strategies for toughening the cement matrix through delivering ductile nanoscale performance of C–S–H to macroscale cementitious materials, and provides superior performance for concrete in existing and new structures.

Dynamical complexity driven by water temperature in a size-dependent phytoplankton–zooplankton model with environmental variability

In this paper, we propose a stochastic phytoplankton–zooplankton (PZ) system modeling the growth dynamic of phytoplankton, where the death rate of zooplankton and the growth rate of phytoplankton are considered to be dependent on water temperature, and random environmental perturbation is characterized by mean-reverting Ornstein–Uhlenbeck process. Mathematical theoretical work mainly studies the stability, persistence, and Hopf bifurcation of deterministic PZ model, as well as analyzes the stochastic extinction, persistence in mean, and ergodic stationary distribution of stochastic PZ model, which provides a theoretical basis for numerical simulations. Numerical simulation work mainly reveals that water temperature plays a crucial role in influencing the dynamic of size-dependent phytoplankton–zooplankton interactions, whether in deterministic or random environments. An important conclusion of this study is that the increase of water temperature is able to induce bubbling effect, however, the small phytoplankton cell size can weaken this bubbling effect, and the large phytoplankton cell size can strengthen this bubbling effect, which supports the experimental conclusion that body size plays a key role in mitigating the negative impact of warming on predator–prey interactions. Further, it is found that the plankton with large body size has the advantages of stable and sustainable survival in the cold environment, while the plankton with small body size has advantages of stable and sustainable survival in the warm environment. Therefore, it can be inferred that plankton will evolve into large body size in the cold environment, but they will evolve into small body size in the warm environment.

Simulation of Thermal Dissipation and Fatigue Properties of a Pressing Sealed Insulated Gate Bipolar Transistor Device

Insulated gate bipolar transistor (IGBT) is widely needed in many applications, such as motors, inverters, express railways, and automobiles. As the integrity level of IGBT increases, thermal management becomes a more significant issue, especially when third-generation semiconductor chips made with SiC or GaN come into application. Here, a press packaging SiC IGBT module is designed, which involves silver sintering. Firstly, we make a thermal analysis using the finite element method (FEM). Then, a heat dissipation plane is optimized. Groups of heat sink materials and airflow velocities are investigated, and an optimized one is chosen as the basis of further fatigue analysis. In the simulated working condition, the maximum temperature can reach 100°C, which could further induce serious problems in fatigue performance. We have analyzed 4 models with different silver layer edge shapes. The best one shows a 27% higher expected fatigue life than the standard one, which shows that the fatigue performance is very sensitive to the edge shape of the silver layer. This research can help further optimization and designing of advanced IGBT devices.

Reliability analysis of a CNT-TF-FinFET for hostile temperature

In the concerned research work, an extensive study has been carried out for a newly developed carbon nanotube truncated fin (CNT-TF) FinFET from an analog as well as a linearity point of view. All the simulated work has been gleaned for n-type FET on Silvaco software for TCAD. Firstly, CNT-TF-FinFET has been combatted against TF-FinFET which gave out the enhanced results as 750 times and 2.13 times for switching ratio and Vth respectively. After perceiving, CNT-TF-FinFET was studied at different temperatures, out of which the device at 200 K achieved zenith with 67.7 %- and 1000-times enhancement in efficiency and IMD3 respectively. The device also shows the linear behavior at higher temperatures in terms of gm3 and VIP3. All these accomplishments made the CNT-TF-FinFET a much better competent to be operated at extreme geographic poles of earth or in outer space with extremely low temperatures.

A new approach for ammonia gas sensing using a source pocket dopingless tunnel FET with PANI catalyst

This article presents a new ammonia (NH3) gas sensor designed using a dopingless tunnel field effect transistor with a SiGe source pocket (SP-DLT) and investigates its sensitivity using a polyaniline (PANI) conducting polymer as the gate catalyst. PANI nanofiber has a greater specific surface area and a linked network structure, which increases its sensing performance as a catalyst. Combining an HfO2 gate dielectric and SiGe source pocket improves the drain current. The sensing film is deprotonated when the PANI conducting polymer is exposed to the gas, reducing the film’s resistance. As a result, more electron or proton transfer is possible, ensuring a more substantial interaction between the sensor and the analyte gas. This modulates the electrical characteristics and work function of the TFET. The sensor’s electrical performance is examined in terms of drain current, average subthreshold swing (SS), electric field, band-to-band tunneling rate (BTBT), and energy band diagram for an ammonia gas pressure (AGP) range of 10−12 torr to 10−9 torr. The sensitivity and selectivity of the proposed PANI-SP-DLT sensor in terms of different metrics, including drain current, average SS, current ratio, transconductance, and gain factor, are further investigated. The study is expanded to consider the impact of temperature affectability and the germanium mole fraction on current sensitivity performance. The Silvaco ATLAS device simulator is used for the brief simulation work for the developed gas sensor.

Modeling ultrasound modulation of neural function in a single cell

BackgroundLow intensity ultrasound stimulation has been shown to non-invasively modulate neural function in the central nervous system (CNS) and peripheral nervous system (PNS) with high precision. Ultrasound sonication is capable of either excitation or inhibition, depending on the ultrasound parameters used. On the other hand, the mode of interaction of ultrasonic waves with the neural tissue for effective neuromodulation remains ambiguous. New methodHere within we propose a numerical model that incorporates the mechanical effects of ultrasound stimulation on the Hodgkin-Huxley (HH) neuron by incorporating the relation between increased external pressure and the membrane induced tension, with a stress on the flexoelectric effect on the neural membrane. The external pressure causes an increase in the total tension of the membrane thus affecting the probability of the ion channels being open after the conformational changes that those channels undergo. ResultsThe interplay between varying the acoustic intensities and frequencies depicts different action potential suppression rates, whereby a combination of low intensity and low frequency ultrasound sonication proved to be the most effective in modulating neural function.Comparison with Existing Methods: Our method solely depends on the HH model of a single neuron and the linear flexoelectric effect of the dielectric neural membrane, when under an ultrasound-induced mechanical strain, while varying the ion-channels conductances based on different sonication frequencies and intensities. We study the effect of ultrasound parameters on the firing rate, latency, and action potential amplitude of a HH neuron for a better understanding of the neuromodulation modality of ultrasound stimulation (in the continuous and pulsed modes). ConclusionsThis simulation work confirms the published experimental data that low intensity and low frequency ultrasound sonication has a higher success rate of modulating neural firing.

A pragmatic retrofitting approach to enhancing the thermal, energy and economic performance of an educational building: a case study in Malaysia

Abstract Building retrofit procedures play a crucial role in improving the energy performance and economic indicators of a building. In this context, an energy audit is typically recommended, but it is seldom used as a comprehensive approach due to the complexity and associated costs. This article aims to conduct a holistic energy audit approach for a university building in Malaysia, with the objective of diagnosing energy efficiency deficiencies, identifying areas of energy waste and proposing practical retrofit measures accordingly. The approach involved multiple stages, including measurements, surveys and simulation work. Eight energy-saving measures were proposed, targeting improvements in envelope elements, cooling and lighting systems, and operation and control. The Design-Builder software was utilized for energy simulation, assessing the annual energy savings. Economic evaluation indices, such as net present value and simple payback period, were used to assess the economic feasibility of the measures. The results demonstrated significant potential for energy reduction, with each measure achieving annual energy reductions ranging from 2% to 18%, and a cumulative impact of 41% on annual energy consumption when combined. The investment payback period for the energy-saving measures varied from 0.8 to 8.9 years, with a payback period of 3.9 years for the combined energy-saving measures. Furthermore, the net present value was positive, indicating the economic feasibility of investing in the proposed energy-saving measures. These findings provide valuable energy-saving opportunities that can be applied to improve similar buildings on the university campus.

Time-of-day effects on speed and accuracy performance during simulated shiftwork

ABSTRACT Performance on tasks involving speed and accuracy fluctuate throughout the 24-h day negatively affecting shift workers and organizations. Two simulated work shifts common in occupational settings were used to assess performance on a vigilance and math task. In study 1, 33 sleep-deprived participants completed a nightshift. In study 2, 32 partially sleep-deprived participants completed a dayshift. These studies found that performance differed between the type of task and the type of simulated shift where performance during the nightshift was worse than during the dayshift. In addition, collapsing speed and accuracy on the math task into inverse efficiency scores provided a unique measure that captured the impact of circadian rhythms during shiftwork. The current study also indicated that participants adopted cognitive strategies including speed-accuracy tradeoff and regulatory foci regarding work motivation (prevention focus and promotion focus) when completing the tasks depending on time-of-day, type of shift, circadian rhythms, and amount of sleep deprivation. This suggests that researchers and organizations should consider cognitive strategies in addition to the physiological components of sleep deprivation and circadian rhythms when investigating and documenting the impact of time-of-day due to different types of shiftwork conditions on performance and safety.

Refractive Index-Modulated LSPR Sensing in 20–120 nm Gold and Silver Nanoparticles: A Simulation Study

Localized surface plasmon resonance (LSPR) based sensing has been a simple and cost-effective way to measure local refractive index changes. LSPR materials exhibit fascinating properties that have significant implications for various bio/chemical sensing applications. In many of these applications, the focus has traditionally been on analyzing the intensity of the reflected or transmitted signals in terms of the refractive index of the surrounding medium. However, limited simulation work is conducted on investigating the refractive index sensitivity of LSPR materials. Within this context, here we simulate the refractive index sensing properties of spherical gold (Au) and silver (Ag) nanoparticles ranging from 20–120 nm diameter within 1.0 to 1.50 refractive index units (RIU). After analyzing the peak optical efficiency and peak wavelength, we report the sensing performance of these materials in terms of sensitivity, linearity and material efficiency, which we refer to as the figure of merit (FOM). Overall, our observations have revealed greatest FOM values for the smallest sized nanoparticles, a FOM of 6.6 for 20 nm AuNPs and 11.9 for 20 nm AgNPs with refractive index of 1.

Modeling Silicon-Dominant Anodes: Parametrization, Discussion, and Validation of a Newman-Type Model

Silicon is a promising anode material and can already be found in commercially available lithium-ion cells. Reliable modeling and simulations of new active materials for lithium-ion batteries are becoming more and more important, especially regarding cost-efficient cell design. Because literature lacks an electrochemical model for silicon-dominant electrodes, this work aims to close the gap. To this end, a Newman p2D model for a lithium-ion cell with a silicon-dominant anode and a nickel-cobalt-aluminum-oxide cathode is parametrized. The micrometer silicon particles are partially lithiated to 1200 mAh gSi−1. The parametrization is based on values from the electrode manufacturing process, measured values using lab cells, and literature data. Charge and discharge tests at six different C-rates up to 2C serve as validation data, showing a root-mean-squared error of about 21 mV and a deviation in discharge capacity of about 1.3%, both during a 1 C constant current discharge. Overall, a validated parametrization for a silicon-dominant anode is presented, which, to the best of our knowledge, is not yet available in literature. For future work, more in-depth studies should investigate the material parameters for silicon to expand the data available in the literature and facilitate further simulation work.

Effects of Search Strategies on Collective Problem-Solving

In today’s dynamic and complex social environments, collaborative human groups play a critical role in addressing a wide range of real-world challenges. Collective problem-solving, the process of finding solutions through the collaboration of individuals, has become imperative in addressing scientific and technical problems. This paper develops an agent-based model to investigate the influence of different search strategies (simple local search, random search, and adaptive search) on the performance of collective problem-solving under various conditions. The research involves simulations on various problem spaces and considers distinct search errors. Results show that random search initially outperforms other strategies when the search errors are relatively small, yet it is surpassed by adaptive search in the long term when the search errors increase. A simple local search consistently performs the worst among the three strategies. Furthermore, the findings regarding adaptive search reveal that the speed of adaptation in adaptive search varies across problem spaces and search error levels, emphasizing the importance of context-specific parameterization in adaptive search strategies. Lastly, the values of Ps=0.9 and Pf=0.2 obtained through human subject experiments in adaptive search appear to be a favorable choice across various scenarios in this simulation work, particularly for complex problems entailing substantial search errors. This research contributes to a deeper understanding of the effectiveness of search strategies in complex environments, providing insights for improving collaborative problem-solving processes in real-world applications.

Changes in Neck and Shoulder Muscles Fatigue Threshold When Using a Passive Head/Neck Supporting Exoskeleton During Repetitive Overhead Tasks.

This study aimed to investigate the effects of a head/neck supporting exoskeleton (HNSE) on the electromyographic fatigue threshold (EMGFT) of the neck and shoulder muscles during a simulated overhead work task. Overhead work is a well-known risk factor for neck and shoulder musculoskeletal disorders due to the excessive strain imposed on the muscles and joints in these regions. Fourteen healthy males performed a repetitive overhead nut fastening/unfastening task to exhaustion while wearing and not wearing the HNSE at two neck extension angles (40% and 80% of neck maximum range of motion). Electromyographic signals were continuously recorded from the right and left sternocleidomastoid (SCMR, SCML), splenius capitis (SCR, SCL), upper trapezius (UTR, UTL), and anterior deltoid (ADR, ADL) muscles. The normalized electromyographic amplitude (nEMG) data was time normalized, and a bisegmental linear regression was applied to determine the muscle fatigue break point. The results showed a significant increase in fatigue threshold time in the SCMR (p < .001), SCML (p = .002), and UTR (p = .037) muscles when the HNSE was used. However, the EMGFT times for the right and left deltoid and left trapezius muscles showed a nonsignificant reduction due to the head/neck support exoskeleton use. In addition, the neck extension angle did not reveal a significant effect on muscles' EMGFT time. Overall, the findings confirmed a significant delay in fatigue onset in sternocleidomastoid muscles, as measured by the electromyographic fatigue threshold. This finding suggests that the HNSE can be an effective ergonomic intervention for reducing the risk of musculoskeletal disorders in overhead workers. However, further studies are needed to investigate the effect of the HNSE at other neck extension angles and more realistic tasks to ensure the generalizability of our results. The present findings emphasize the application of the fatigue onset time to evaluate the effectiveness of ergonomic interventions, including exoskeletons, which can subsequently be utilized to alleviate postural demands and reduce the risk of musculoskeletal disorders.

High-fidelity burns moulage makes simulations “Come Alive”

IntroductionThe simulation community knows immersive clinical simulation works, especially high-fidelity simulations that portray situations close to real-life. The question remains: what is the role of moulage in the effectiveness of this strategy? MethodThis article describes students' perceived fidelity of moulage used during a burns simulation activity. In a mixed method study, 85 nursing students completed the Moulage Authenticity Rating Scale (MARS) and responded to two open-ended questions that explored emotional fidelity. ResultsMost respondents rated the moulage burns high in fidelity. Respondents' senses of sight, touch, hearing, and smell were triggered giving way to intense emotions. However, sensory overload may have led to the students missing clinical cues. ConclusionThe moulage met the criteria for physical, conceptual, and emotional fidelity, contributing to students’ engagement during the simulation activity. Educators could improve student engagement by enhancing the fidelity of simulation activities through moulage whilst considering the cognitive load.