High-fidelity Simulation Research Articles

Data-Driven Surrogate Modeling with Microstructure-Sensitivity of Viscoplastic Creep in Grade 91 Steel

AbstractTo support the development of advanced steel alloys tailored to withstand extreme conditions, it is imperative to account for the mechanical performance of components, while considering the influence of local microstructure on the macroscopic response. To this end, this study focuses on the development of microstructure-sensitive constitutive models for the mechanical response of Grade 91 steel exposed to extreme thermo-mechanical environments. Polynomial chaos expansion (PCE) surrogates are used to emulate high-fidelity polycrystal simulations of the viscoplastic response of Grade 91 steel as a function of the microstructure fingerprint (e.g., dislocations and precipitates). To cover a wide temperature–stress domain, two separate PCE surrogates—one that captures softening and the other that captures hardening behavior—are combined using another (sparse) Gaussian process regression model. The resulting constitutive creep surrogate model is integrated within the MOOSE finite element framework to simulate the intricate effects of microstructure, in particular MX-phase precipitates, on a component with a graded microstructure. Surrogate sensitivity analysis is applied to quantify the relevant impact of spatially varying microstructure on the creep response in a test-case involving a Grade 91 alloy with a prototypical weld.

Optimizing ambulance nurse performance in southern Tuscany: insights from a Delphi study

This study aims to enhance the performance of ambulance nurses in Southern Tuscany through a Delphi method analysis. Conducted from January to December 2022, the research engaged 16 expert participants from a Tuscan health agency. Data on 89 interventions performed by "India" ambulances, which are nurse-led emergency vehicles, were collected. The Delphi method facilitated structured communication among the experts over three rounds, resulting in consensus on nine key intervention proposals. These proposals span areas including staff training, measurement and data collection methods, technologies and materials, and work environments. Key recommendations include prioritizing high-fidelity simulation training, improving data collection for clinical risk, and standardizing equipment across all emergency units. The study highlights the importance of continuous professional development, technological integration, and supportive work environments to optimize the effectiveness of pre-hospital emergency care. These findings provide a practical roadmap for enhancing the competencies and performance of ambulance nurses, ultimately improving patient outcomes in emergency situations.

Transport properties of high Mach number hypersonic air plasmas

Abstract The collisional and transport properties of hypersonic plasmas have been simulated by an air plasma chemistry model, which includes collisions with electrons, ions and neutral species, elastic scattering, vibrational relaxation and a transport model. Species and thermal fluxes at the surface of the vehicle have been investigated as a function of post-shock gas temperature and species sticking coefficient for a fixed altitude of 50 km. At temperatures below about 5,000 K, the leading species are vibrationally excited nitrogen molecules, while at higher temperatures the molecular nitrogen and atomic oxygen dominate. Large fluxes of oxygen atoms have been observed in the simulations, on the order of 10^20 cm^-2s^ -1, which may lead to high rates of surface oxidation. Another subject of this study is temperature equilibration. For electrons, equilibration takes tens of microseconds, while the vibrational temperature equilibration time vary widely; from a few microseconds to hundreds of microseconds, depending on the gas temperature. Finally, the adiabatic parameter  was calculated for post-shock gas temperatures 5,000 K and 10,000 K and total gas density 1.2×10^17 cm^(-3). For post-shock gas temperature 5,000 K, only a slight reduction of gamma from the ideal gas value of ~1.4 is observed, while for 10,000 K it drops to ~1.26, which has a considerable impact on plasma properties. It was further established that the vibrational kinetics plays a leading role. While a single excited vibrational level of the N2 molecule captures the salient properties of the plasma, a detailed vibration kinetics is required for high-fidelity simulations.

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.

The effects of cognitive training on driving performance.

Driving is a complex task necessitating an intricate interplay of sensory, motor, and cognitive abilities. Extensive research has underscored the role of neurocognitive functions, including attention, memory, executive functions, and visuospatial skills, in driving safety and performance. Despite evidence suggesting cognitive training's potential in enhancing driving abilities, comprehensive cognitive training's impact on driving performance in young adult drivers remains unexplored. Our study aimed to fill this gap by implementing an intensive, 8-week, multidomain computerized cognitive training program and assessing its transfer effects on the driving performance of young adult drivers, using a high-fidelity simulator. The study employed a randomized controlled trial design, with passive control group. The mixed-design analysis of variance (ANOVA) revealed a notable interaction between the time of testing and the respective participant groups concerning driving performance. Post hoc analyses showed that, compared to the control group, participants undergoing cognitive training demonstrated significantly fewer traffic infractions in the post-training evaluation. These findings suggest that cognitive training could be a useful tool for enhancing driving safety and performance in young adult drivers. Further research should aim to address the limitations posed by the absence of an active control group.

Hydrodynamic Shape Optimization of a Naval Destroyer by Machine Learning Methods

This paper explores the integration of advanced machine learning (ML) techniques within simulation-based design optimization (SBDO) processes for naval applications, focusing on the hydrodynamic shape optimization of the DTMB 5415 destroyer model. The use of unsupervised learning for design-space dimensionality reduction, combined with supervised learning through active learning-based multi-fidelity surrogate modeling, allows for significant improvements in computational efficiency while addressing complex, high-dimensional design spaces. By applying these ML techniques to both single- and multi-objective optimizations, aimed at minimizing resistance and enhancing seakeeping performance, the proposed framework demonstrates its practical value in hydrodynamic design. This approach provides a scalable and efficient solution, reducing the reliance on high-fidelity simulations while accelerating the optimization process, without substantial modifications to existing toolchains. A design-space dimensionality reduction of approximately 70% is achieved, reducing the design variables from 22 to 7 while retaining 95% of the original geometric variance. Additionally, computational cost reductions of 65% to 98% are observed, compared to using the full design space and high-fidelity simulations only.

Evaluating anxiety and self-confidence in nursing students: Adaptation and validation of the NASC-CDM scale for high-fidelity simulation training

BackgroundAnxiety and self-confidence impact performance and decision-making during high-fidelity simulation in nursing education. A validated tool to assess these factors is lacking. MethodsThe Nursing Anxiety and Self-Confidence with Clinical Decision-Making© scale (NASC-CDM) was translated, adapted for high-fidelity simulation and validated using a five-step approach. This included a Content Validity Index review by experts and pilot-testing with first-year nursing students. Construct validity and internal consistency were evaluated using Exploratory Factor Analysis and Cronbach's alpha. ResultsA total of 187 first-year nursing students participated. Exploratory Factor Analysis identified constructs for anxiety (two factors, 60.72% variance) and self-confidence (three factors, 61.22% variance). The tool demonstrated high internal consistency, with Cronbach's alpha values of 0.956 for anxiety and 0.955 for self-confidence. ConclusionThe 19-item Dutch Nursing Anxiety and Self-Confidence with Clinical Decision-Making for High-Fidelity Simulation scale is a valid and reliable instrument measuring anxiety and self-confidence in nursing students during high-fidelity simulation.

Implementing an obstetric simulation training protocol in a critical access hospital

BackgroundA high-fidelity (HF) simulation training protocol was implemented to improve nurses' knowledge, skills, and self-confidence to improve patient quality of care and safety, thus ultimately having an impact on decreasing maternal mortality rates. SampleThe sample consisted of 16 participants including nurses, a physician, emergency medical technicians (EMTs), and paramedics. MethodsThis project used a self-confidence and competence assessment prior to and following participation to determine statistical significance. In addition, review of the National League for Nursing Student Satisfaction and Self-Confidence in Learning tool data was done to determine the overall success of implementing a HF simulation training protocol. ResultsSuccess of the simulation and content was determined by a reported increase in self-confidence and competence post-simulation. The summative evaluation revealed that participants felt that implementing a HF simulation training protocol, in general, was useful for training. ConclusionWhile this project focused on obstetrics, the premise of the project can be applied to many subject areas to advance practice in rural health. Findings suggest that use of a high-fidelity simulation training protocol is beneficial.

Thermal performance analysis of PCM-integrated structures using the resistance-capacitance model: Experiments and numerics

While holding significant potential to reduce cooling energy requirements in buildings, the incorporation of phase-change materials in building envelopes requires information regarding their diurnal and seasonal behaviour through high-fidelity simulations. However, utilizing short-term simulations and experimentation using state-of-the-art models for such a complex configuration does not accurately represent the true heat transfer dynamics of the building. To address this lacuna, we present a physics-based, low computational cost, experimentally and numerically validated resistance–capacitance (RC) model specifically tailored for PCM-encapsulated structures, designed for long-term simulations. The validation of this model is conducted through in-house experiments. Additionally, we support the credibility of our RC model by subjecting it to validation through 3D numerical simulations, emphasizing its precision and reliability. Then, we use the validated model to optimize the thermal properties of concrete roofs in hot and dry climates, taking a specific instance of a city in Western India for various geometric configurations as an illustrative example. We find that a PCM with a phase change temperature between 37 and 42 °C can reduce the peak ceiling temperature by up to 10 °C and the peak energy ingress by a factor of 2 or more, in a typical roof element subjected to the prevailing climatic conditions during peak summer. This shows the time constant of the modified roof is effective in delaying and damping the imposed solar insolation. We make specific recommendations on the selection and geometry optimization of PCM-incorporated roof elements.

Advanced graph neural network-based surrogate model for granular flows in arbitrarily shaped domains

In next-generation manufacturing, the introduction of a surrogate model is crucial for constructing a digital twin. This trend is no exception in powder industry. Various mesh-free approaches such as the discrete element method (DEM) are versatile tools for facilitating simulation-based digital twins of powder industries. Unfortunately, the DEM-based high-fidelity simulation of actual industrial processes poses a significant challenge because of the prohibitive computational costs. In this context, the surrogate model has emerged as a promising technique to solve this problem. However, surrogate modeling of powder processes within arbitrarily shaped boundaries is substantially impossible. To resolve this essential problem in the existing surrogate models, we propose a novel surrogate model, namely, a signed distance function-based graph neural network (SGN), to accelerate the simulations of granular flows. In the SGN, the wall boundary is efficiently modeled by combining the graph structure with a signed distance function. Validation studies were conducted in typical powder systems, such as a sandglass, and a rotating-paddle mixer. A systematic comparison of the simulation results between the high-fidelity and SGN simulations shows that the proposed SGN accurately simulates granular dynamics, with a remarkable increase in computational efficiency. This study significantly contributes to the next-generation manufacturing in the powder industry.

Improving Interprofessional Provider Perceptions About Opioid Use Disorder in the Acute Care Setting Through a Blended Educational Simulation Intervention

Background The influx of patients in the acute care setting with opioid use disorder (OUD) has outpaced many hospitals’ ability to educate interprofessional staff. This creates distressing experiences for interprofessional staff and patients, leading to moral distress and burnout in staff and poor patient outcomes. Objective The purpose of this study was to improve interprofessional staff knowledge, attitudes, and perceptions toward working with patients who have OUD using a blended classroom-simulation–based curriculum. Methods A preintervention and postintervention design was selected. Interprofessional staff (n = 46) participated in a blended classroom-simulation educational intervention focused on the neurobiology of OUD, stigma reduction, pain management, and harm reduction principles, followed by 3 high-fidelity simulation scenarios. Participants completed the Drug and Drug Problems Perceptions Questionnaire to measure staff self-perceived knowledge, skills, and attitudes when working with patients who use drugs before, immediately after, and again 3 and 6 months postintervention. Results Most respondents were nurses with an average of 7.6 (SD, 9.6) years of experience. The majority did not have prior training in substance use disorder before (75.6%). There was a statistically significant decrease in mean Drug and Drug Problems Perceptions Questionnaire scores across the total score mean: 55.2 (95% confidence interval, 52.2-58.3) versus 45.5 (95% confidence interval, 43.9-47.1), P < .001. Decreased score indicates improved attitude and perception. Discussion A curriculum consisting of a blended classroom-simulation intervention was successful at improving several domains regarding perceptions of caring for patients with OUD. This educational intervention can serve as a model for health care systems with goal of improving patient outcomes and staff well-being.

Workpiece temperature control in friction stir welding of Inconel 718 through integrated numerical analysis and process control

Friction stir welding (FSW) offers significant advantages over fusion welding, particularly for high-strength alloys like Inconel 718. However, achieving optimal surface quality in Inconel 718 FSW remains challenging due to its sensitivity to temperature fluctuations during welding. This study integrates finite element simulations, statistical analysis, and advanced control methodologies to enhance weld surface quality through adequate thermal management. High-fidelity simulations of the FSW process were conducted using a validated 3D transient COMSOL Multiphysics model, producing a comprehensive dataset correlating process parameters (rotational speed, axial force, and welding speed) with workpiece temperature. This dataset facilitated statistical analysis and parameter optimization through Analysis of variance (ANOVA) method, leading to a deeper understanding of process variables. A nonlinear state-space system model was subsequently developed using experimental data and the system identification toolbox in Matlab, incorporating domain-specific insights. This model was rigorously validated with an independent dataset to ensure predictive accuracy. Utilizing the validated model, tailored control strategies, including proportional-integral-derivative (PID) and model predictive control (MPC) in both single and multivariable configurations, were designed and evaluated. These control strategies excelled in maintaining welding temperatures within optimal ranges, demonstrating robustness in response times and disturbance handling. This precision in thermal management is poised to significantly refine the FSW process, enhancing both surface integrity and microstructural uniformity. The strategic implementation of these controls is anticipated to substantially improve the quality and consistency of welding outcomes.

Development and Validation of a Low-Cost, High-Fidelity Simulation Model for Robotic Internal Mammary Artery Harvest Using the da Vinci Xi Robot.

We created and validated a low-cost simulation model for robotic internal mammary artery (IMA) takedown. The simulation model utilized a calf fetus thorax cavity stented open internally and secured to a table. The simulation model was validated at a 2-day robotic cardiac surgery workshop. Each participant harvested one IMA using the da Vinci Xi robot (Intuitive Surgical, Sunnyvale, CA, USA). We compared participant self-reported confidence at robotic IMA harvest before and after using the simulator. Our novel thorax-securing strategy resulted in a stable structure and allowed access to both IMAs from the same 3 ports. The cost to set up the first simulation model was $176 and $133 for every subsequent model. Fifty participants used the simulation model: 42 cardiothoracic surgery attendings and 8 fellows or residents. The feedback form response rate was 78% (n = 39). On the Likert scale, participants rated realism of the calf model to simulate robotic IMA harvesting (0 = not realistic, 10 = highly realistic) with a median of 8 out of 10 (interquartile range [IQR] 7 to 9). Participant confidence (0 = not at all confident, 10 = very confident) in robotic IMA harvesting before and after using the simulator increased (P = 0.001) from a median of 5 (IQR 1 to 7) to 9 (IQR 7 to 10). This robotic IMA harvest simulation model is affordable, realistic, and improved participant confidence in robotic IMA harvest. It may provide a valuable training tool for surgeons learning robotic coronary bypass surgery and allows for training frequency necessary to pass basic learning curves.

Modelling Sea-Surface Wave Motion and Ship Response Using Smoothed Particle Hydrodynamics and Finite Element Analysis

The response of a ship or other vessel to surface sea waves, including extreme waves, may compromise crew and vessel safety and long-term operational capability. Herein, a novel high-fidelity numerical time-dependent simulation approach is presented using Smoothed Particle Hydrodynamics (SPH) for modelling sea waves coupled with Finite Element Analysis for modelling vessel structural response under wave loading conditions. The results are compared with physical scale model wave tank test results. Good agreement was obtained for heave and pitch motions and vertical bending moments for various forward (head) speeds in regular head waves, heave and pitch motions, and vertical bending moments. High computational demands can be met by the increasing availability of computation power. Ongoing research is outlined. The implications for the design of vessels such as ships and for through-life assessment are discussed.

AI-driven adaptive analysis for finding emergent behavior in military capability design

This article introduces and demonstrates a new methodology for searching for emergent behavior in simulation-based analysis as rare, extreme events using adaptive techniques enabled by recent advances in Bayesian machine learning (ML). The new methodology, Low-cost Adaptive exploratioN to Track down Extreme, Rare events using Numerical optimization (LANTERN), supports analysis activities in defense planning that iteratively build up the understanding of new technology and concept alternatives in complex military scenarios. Central to this process is emergent behavior—hard-to-predict but highly important behaviors that present problems or opportunities. These unexpected behaviors can be generated in complex military scenarios and are crucial to decision-making, but are often difficult to find and work with due to the expense and cost of the existing approaches for working with high-fidelity military simulation. To address this challenge, LANTERN is formulated to accelerate the discovery of emergent behavior as rare, extreme events by combining human expert understanding with new artificial intelligence (AI)-driven adaptive experimentation techniques in iterative analysis. A demonstration of the methodology is presented using military agent-based simulation scenarios developed in the Advanced Framework for Simulation, Integration, and Modeling (AFSIM). The demonstration highlights how analysis can focus directly on searching for emergent behavior and shows substantial improvements over brute-force Monte Carlo approaches.

Nursing Students’ Satisfaction with Clinical Simulation: A Cross-Sectional Observational Study

Clinical Simulation improves results in the students’ learning tests and allows for preserving acquired knowledge for longer periods of time, promoting more significant learning. This study was conducted to analyze Nursing students’ satisfaction with Clinical Simulation in three centres attached to a university from southern Spain. Methods: A quantitative, non-experimental and cross-sectional descriptive study was carried out. The students included were attending their third year of the Nursing undergraduate course and had already taken part in training sessions by means of Clinical Simulation. The Satisfaction Scale with High-Fidelity Clinical Simulation in Students (SSHF) was used for data collection. This scale has been validated and has 33 items grouped into eight factors. The SPSS software (version 28), was used for data analysis, establishing p-values < 0.05 for the statistically significant differences. Results: The participants were 180 students, with a mean age of 22.17 years old. Of them, 90.56% belonged to the female gender. A mean score of 3.82 out of 5 was obtained in the SSHF items. The items that obtained the highest scores were the following: benefits of Clinical Simulation as it relates theory with practise; possibility of learning based on the mistakes made; and comfort and respect while the sessions were developed. The item that obtained the lowest score was “timing for each simulation case”. We found significant differences in the results obtained according to each attached centre. Conclusions: The students showed high satisfaction levels regarding High-Fidelity Clinical Simulation in each of the three attached centres included in the study. Nevertheless, they stated the need to invest more time in Clinical Simulation sessions.

Multidisciplinary Optimization of Gyroid Topologies for a Cold Plate Heat Exchanger Design

Abstract The strive to reduce the environmental impact of aviation has led to electrification and increasing demand for powerful on-board power electronic systems. These high-performance electrical components are bound to produce significant amounts of low-quality heat waste that, if not dissipated properly, will lead to malfunctioning and even permanent damage. For this reason, high-performance heat exchangers (HX) represent a key enabler for future advances in aircraft systems electrification and are vital to meet net zero goals and reduce the aviation's carbon footprint. For a given volume of the exchanger, the heat flow rate can be increased by adopting more sophisticated fluid domains. However, excessive geometrical complexity will lead to an increase in pressure losses, often resulting in inhomogeneous temperature distributions. In this paper, a novel optimization procedure is employed to maximize the efficiency of a high-performance heat exchanger, while minimizing overall pressure loss and temperature gradients. The optimization is performed with full three-dimensional high-fidelity computational flow simulations. The geometry of the fluid domain is constituted by triply periodic minimal surfaces (TPMS), with a parametrization based on thickness and aspect ratios, done by using the ntopology suite. To assess the performance gain, the topology-optimized design is compared against the datum case and a conventional serpentine design.

LES and RANS Modeling of an 84-Pin Hexagonal Rod Bundle with Spacer Grid and Experimental Validation

As part of an ongoing integrated research project (IRP), detailed investigations of the flow characteristics of an 84-pin hexagonal rod bundle representing a potential modular gas-cooled fast reactor design have been performed. The rod bundle features a pitch-to-diameter ratio of 1.5 and a large central guide tube, along with simple spacer grids and an outer enclosure. The primary focus of the current work is modeling and simulation of this geometry using multiple computational techniques. These include high-fidelity large eddy simulation (LES) and advanced Reynolds-averaged Navier-Stokes (RANS) approaches. The flow predictions are validated at a Reynolds number of 12 000 using matched index of refraction particle image velocimetry experimental data that were also generated by Texas A&M University as part of the IRP. The comparison data include velocity magnitude and turbulent kinetic energy (TKE) at different lateral locations and at multiple distances downstream of the spacer grid. Many characteristics of the experimental flow are replicated in the LES and RANS runs, and a general agreement between simulation and experiment is shown. Except for the immediate vicinity of the grid, LES is able to capture the velocity magnitude within a 10% error and the TKE to within the experimental uncertainty. The LES shows notably better agreement with the experimental data than RANS, particularly regarding the TKE decay behavior downstream of the grid. Both methods show significant departures from the experiment in their predictions close to the central guide tube. The experimental and high-fidelity data will be used to inform closures for novel multiscale methodologies. The long-term goal of the work is to use the high-fidelity data to yield improved multiscale thermal analysis techniques for solving fuel performance problems of direct relevance to industry.

Analysis and evaluation of suitability of high-pressure dynamic constitutive model for concrete under blast and impact loading

Concrete demonstrates complex dynamic mechanical behaviors under the influence of blast or impact loads, and there are inherent limitations in experimental and theoretical methods when dealing with highly nonlinear problems. As computational technologies and mechanics continue to evolve, it is possible to conduct high-fidelity simulations of the transient response of concrete structures subjected to intense dynamic loads. Such simulations play a crucial role in revealing the propagation laws of stress waves, the progression of damage, the mechanisms of structural failure, and in conducting protective engineering design. An accurate concrete material model is essential for conducting numerical studies. This article reviews the development of high-pressure dynamic constitutive models for concrete in recent years from both experimental research and theoretical modeling perspectives, focusing on the analysis and evaluation of the modeling methods and main shortcomings of the equation of state(EOS), strength model, and damage model. Using single-element numerical simulations under a single loading path and numerical calculations of engineering cases under complex loading paths, a systematic analysis and comparison were conducted on the predictive capabilities of the HJC, RHT, KCC, and CSC models, as well as the newly developed Kong-Fang and Yan-Chen models. It pointed out the impact of high-pressure mechanical behavior of concrete and the cumulative effect of damage under hydrostatic pressure on the calculation results. Finally, a discussion was conducted on the inherent flaws, applicability, and research difficulties of local constitutive models of concrete in the finite element method. This provides a reference for the selection and research of constitutive models when conducting numerical analysis of the blast and impact resistance of concrete structures.

Nursing and midwifery simulation training with a newly developed low-cost high-fidelity placenta simulator: a collaboration between Italy and Ethiopia

BackgroundSimulation training provides safe environment for skill acquisition and retention. This study addresses a critical challenge in Africa – umbilical cord and placenta management after childbirth – aiming to bridge theoretical learning with practical experiences through simulation. We realized a new low-cost high-fidelity simulator of placenta and umbilical cord. We conducted a needs-based training course for nursing and midwifery students at St. Luke Hospital of Wolisso, Ethiopia, to validate our new simulator and compare its acceptability and teaching effectiveness with other two simulators (conventional low-fidelity model and human placenta).MethodsWe surveyed St. Luke Hospital medical experts to obtain their feedback on the new simulator’s face, content, and usability. We carried out a simulation training course for 67 students who received theoretical lectures and simulation courses being divided into three groups according to the simulator used. We assessed the simulators’ user acceptability using the Technology Acceptance Model (TAM) and compared the final objective evaluations by tutors between groups.ResultsExperts confirmed the new simulator’s fidelity, material quality, and usability. Students training on the new simulator demonstrated higher objective scores and perceived it as more useful and user-friendly compared to human placenta, while there was no difference between conventional simulator and human placenta in the TAM items.ConclusionWe validated a new high-fidelity simulator developed by the Sant’Anna School of Advanced Studies in Pisa, Italy, using the TAM scale and robust statistical methods, thanks to a successful collaboration with St. Luke’s Hospital in a simulation training course where students achieved higher objective scores and perceived the simulator as more useful and easier to use than a real human placenta, suggesting significant educational benefits and potential for future research.