High-fidelity Simulation Research Articles

HMAE: a high-fidelity multi-agent simulator for economic phenomenon emergence

Economic models based on multi-agents are increasingly attracting attention and can provide a new perspective for exploring the causes behind social phenomena at the individual level. Existing research usually adopts society-level learning methods, and more research on micro-level heterogeneity among individuals is needed. For this, we propose a high-fidelity multi-agent economy (HMAE) model based on evolutionary game theory, including three types of agents: workers, firms, and the government. In particular, we characterize worker heterogeneity regarding laziness factors, work endowments, and commuting distances. These agents continuously and iteratively update their strategies by randomly exploring and imitating their neighbors to maximize their utility value. We simulated the evolution process of agent behavioral decisions through experiments and found that individual heterogeneity can significantly affect the decisions of workers and firms. These phenomena are consistent with some economic evolution trends in real life, and our research can provide an analytical tool for analyzing the causes of emerging economic phenomena.

Using simulated bronchoscopy to improve training on intensive care units in a north London NHS trust

Flexible bronchoscopy is a vital tool that is used for both diagnostic and therapeutic indications on the intensive care unit. During the COVID pandemic, training opportunities to perform such a skill were decreased and novel methods of teaching, including using bronchoscopy simulators, were explored out of necessity. The aim of this quality improvement project was to investigate how training on an intensive care unit prepared trainees for carrying out bronchoscopy, and whether a simulated bronchoscopy session with a high-fidelity simulator could be a useful adjunct to gain experience. Our pilot, although limited in patient numbers, suggested that current teaching methods might not be adequate for building trainee confidence and that a session with a bronchoscopy simulator could serve as a remedy to this with a significant increase in self-reported confidence. Moving forwards in a post-pandemic era, this serves as a lesson learnt from COVID and would benefit from further development in this area.

Application of transformer architectures in generative video modeling for neurosurgical education.

This article explores the potential impact of OpenAI's Sora, a generative video modeling technology, on neurosurgical training. It evaluates how such technology could revolutionize the field by providing realistic surgical simulations, thereby enhancing the learning experience and proficiency in complex procedures for neurosurgical trainees. The study examines the incorporation of this technology into neurosurgical education by leveraging transformer architecture and processing of video and image data. It involves compiling a neurosurgical procedure dataset for model training, aiming to create accurate, high-fidelity simulations. Our findings indicate significant potential applications in neurosurgical training, including immersive simulations for skill development and exposure to diverse surgical scenarios. The technology also promises to transform assessment and feedback, introducing a standardized, objective way to measure and improve trainee competencies. Integrating generative video modeling technology into neurosurgical education marks a progressive step toward enhancing training methodologies. Despite challenges in technical, ethical, and practical domains, continuous development and evaluation could lead to substantial advancements in surgical education, preparing neurosurgeons more effectively for their demanding roles.

Neural networks based surrogate modeling for efficient uncertainty quantification and calibration of MEMS accelerometers

This paper addresses the computational challenges inherent in the stochastic characterization and uncertainty quantification of Micro-Electro-Mechanical Systems (MEMS) capacitive accelerometers. Traditional methods, such as Markov Chain Monte Carlo (MCMC) algorithms, are often constrained by the computational intensity required for high-fidelity (e.g., finite element) simulations. To overcome these limitations, we propose to use supervised learning-based surrogate models, specifically artificial neural networks, to effectively approximate the response of MEMS capacitive accelerometers. Our approach involves training the surrogate models with data derived from initial high-fidelity finite element analyses (FEA), providing rich datasets to be generated in an offline phase. The surrogate models replicate the FEA accuracy in predicting the behavior of the accelerometer under a wide range of fabrication parameters, thereby reducing the online computational cost without compromising accuracy. This enables extensive and efficient stochastic analyses of complex MEMS devices, offering a flexible framework for their characterization. A key application of our framework is demonstrated in estimating the sensitivity of an accelerometer, accounting for unknown mechanical offsets, over-etching, and thickness variations. We employ an MCMC approach to estimate the posterior distribution of the device’s unknown fabrication parameters, informed by its response to transient voltage signals. The integration of surrogate models for mapping fabrication parameters to device responses, and subsequently to sensitivity measures, greatly enhances both backward and forward uncertainty quantification, yielding accurate results while significantly improving the efficiency and effectiveness of the characterization process. This process allows for the reconstruction of device sensitivity using only voltage signals, without the need for direct mechanical acceleration stimuli.

Nurse-midwifery students’ experiences in high-fidelity simulation: a focus group study

Introduction: Active teaching methodologies, particularly those using high-fidelity simulation, stand out in nursing education for their ability to facilitate the development of students' technical-scientific and non-technical skills, as well as prepare them for clinical practice. High-fidelity simulation using realistic models is an effective tool for improving students' communication skills, leadership and critical thinking. This study aims to explore nurse-midwifery students’ learning experiences in high-fidelity simulation.Methods: Explorative qualitative study using two focus groups interviews with nurse-midwifery students at the Higher School of Nursing. Qualitative data were analyzed using content analysis. Results: Two main themes emerged from content analysis: (i) Simulation-based learning and (ii) Simulation-based teaching. The first theme includes three categories: development of technical-scientific skills, development of non-technical skills, and preparation for internship. The second theme also includes three categories: enhancing theoretical knowledge, facilitating teaching, and post-scenario discussion (debriefing). Conclusions: The use of high-fidelity simulation as a teaching strategy in the training of nurse-midwifery students contributed to the development of technical and non-technical skills essential to nursing practice

Changes in forklift driving performance and postures among novices resulting from training using a high-fidelity virtual reality simulator: An exploratory study

Virtual reality (VR) has emerged as a promising tool for training. Our study focused on training for forklift driving, to address an ongoing worker shortage, and the unknown impact of repeated VR training on task performance and kinematic adaptations. We trained 20 novice participants using a VR forklift simulator over two days, with two trials on each day, and including three different driving lessons of varying difficulties. Driving performance was assessed using task completion time, and we quantified kinematics of the head, shoulder, and lumbar spine. Repeated training reduced task completion time (up to ∼29.8% of initial trial) and decreased both kinematic variability and peak range of motion, though these effects were larger for lessons requiring higher precision than simple driving maneuvers. Our results highlight the potential of VR as an effective training environment for novice drivers and suggest that monitoring kinematics could help track skill acquisition during such training.

Closing the Trauma Performance Improvement Loop With In-situ Simulation

IntroductionContinuous performance improvement (PI) programs are essential for excellent trauma care. We incorporated PI identified from trauma cases into an in-situ simulation-based medical education curriculum. This is a proof-of-concept study exploring the efficacy of high-fidelity pediatric trauma simulations in improving self-reported provider comfort and knowledge for identified trauma PI issues. MethodsThis study was performed at an American College of Surgeons-verified Level I Pediatric Trauma Center. Several clinical issues were identified during the trauma PI process, including management of elevated intracranial pressure in traumatic brain injury and the use of massive transfusion protocol. These issues were incorporated into a simulation-based medical education curriculum and high-fidelity in-situ trauma mock codes were held. In-depth debriefing sessions were led by a senior faculty member after the simulations. The study participants completed pre- and postsimulation surveys. Univariate statistics are presented. ResultsTwenty three providers completed surveys for the pediatric trauma simulations. Self-reported provider confidence Likert scale improved from pre- to postsimulation (P = 0.02) and trauma experience and knowledge scores improved from 82% presimulation to 93% postsimulation (P = 0.02). ConclusionsHigh-fidelity pediatric trauma simulations enhance provider comfort, knowledge, and experience in trauma scenarios. By integrating high-fidelity trauma simulations to address clinical issues identified in the trauma PI process, provider education can be reinforced and practiced in a controlled environment to improve trauma care. Future studies evaluating the implementation of clinical pathways and patient outcomes are needed to demonstrate the effectiveness of simulations in PI pathways.

WP6.4 - General Surgery Bootcamp: Improving Induction for Higher General Surgery Training

Abstract Aim To create an introductory course or “bootcamp” to assist new ST3s transition from working at core trainee to General Surgical Registrar level. Method Using previous expertise of “bootcamps” delivered in the region in similar specialities, a 3-day programme was designed to offer training in areas aligned to the curriculum and considered useful to newly starting higher surgical trainees. The course involved a mix of simulation-based workshops, lectures and skill-based labs. The programme was delivered by a senior faculty directing activity with support from industry partners and medical education/simulation expertise. A range of skills were targeted using animal, virtual and augmented reality models, low and high-fidelity simulation, and non-operative technical skills (NOTSS) in simulated ward and theatre environments. Results 26 trainees (23 new General Surgery ST3s, 3 returning to training) completed the bootcamp. Feedback questionnaires using a likert scale demonstrated clear improvements in trainees’ own skill perception in endoscopy, laparoscopy, open surgery and NOTSS. 100% of responding trainees’ were confident or strongly confident post course, compared to 69% before. We also showed an increase in confidence in laparoscopic suturing (77%), duodenal ulcer repair (69%) and operating in a stressful environment. Conclusions A transitional “bootcamp” can provide both tangible and intangible benefits. This project showed clear improvement in trainee confidence both in key practical and non-practical skills such as communication. It also enabled registrars to familiarise themselves with new colleagues, facilitating an early welcome from the region and embedded support network.

SP8.12 - Evaluating the pig tissue model for acute proctology simulation at General Surgery ST3 Bootcamp

Abstract Evaluating the pig tissue model for acute proctology simulation at General Surgery ST3 Bootcamp Background Acute proctology is an important skill for any general surgical trainee; however it is a skill which often is overlooked when it comes to training. Most, if not all teaching happens during patient procedures. Simulation in this area is underdeveloped and there is a lack of validated models for simulation. These models are also usually expensive and difficult to obtain. The aim of this study was to assess face validity of a low-cost model for teaching acute proctology during the ST3 general surgical bootcamp. Methods We created a model using a pig rectum and perineum. This provided two uses. Firstly, we were able to teach relevant clinical anatomy. Secondly, we were able to simulate EUA, rigid sigmoidoscopy, decompression of volvulus and haemorrhoidal banding. The models allowed for repeated use. Results Twenty-five trainees took part in this simulation. Overall, the trainees felt the rectal examination was realistic (76%), the sigmoidoscope moved realistically in the model (72%) as did the proctoscope (88%). In terms of ability, trainees felt the simulation improved their confidence in EUA (68%), rectal examination (72%) and banding (80%). A total of 80% of trainees thought the model was overall “Good or Excellent”. Conclusions Our study demonstrates that this low-cost, high-fidelity simulation model has a high face validity and may be explored for the use for training new general surgical registrars.

Effect of Atmospheric Stability on Meandering and Wake Characteristics in Wind Turbine Fluid Dynamics

This study investigates the impact of atmospheric stability on wind turbine flow dynamics, focusing on wake deflection and meandering. Using the high-fidelity large-eddy simulation coupled with the Actuator Line model, we explore three stability conditions for the Vestas V80 turbine, both with and without yaw. The results indicate that wake meandering occurs predominantly along the deflected wake axis. Despite varying wake deficits and meandering behaviors, neutral and stable conditions exhibit similar wake deflection trajectories during yawed turbine operations. Spectral analysis of meandering reveals comparable cutoff and peak frequencies between neutral and stable cases, with a consistent Strouhal number (St=0.16). The unstable condition shows significant deviations, albeit with associated uncertainties. Overall, increased stability decreases both oscillation amplitude and frequency, highlighting the complex interplay between atmospheric stability and wind turbine wake dynamics.

Fostering Moral Resilience: Evaluating a High-Fidelity Ethics Simulation With Prelicensure Nursing Students in Their Practice as New Graduates.

Despite the high prevalence of moral distress in nursing, empirical evidence is lacking about the best educational approaches to foster moral resilience. This study evaluates the effectiveness of an ethics simulation in a prelicensure program, 6months into nursing practice. We used an exploratory design, including 2 instruments and open-ended questions. One instrument measured moral distress, the other measured moral resilience. Alumni who participated in the simulation were compared to alumni who received didactic teaching only. Six months into practice, the quantitative data from this study did not show a statistical significance between those who received didactic training only and those who received a simulation, except for one element of the moral resilience scale (relational integrity), in which the control group had a higher score. Open-ended questions confirmed that alumni recognized the positive impact of the simulation in practice. Further study is needed to determine the best educational strategies for teaching ethics in prelicensure nursing programs, with a focus on improving retention and resilience in practice.

How Students Learn during the Pre-Briefing and Observation of Facilitation in a High-Fidelity Patient Simulation: A Narrative Analysis.

The rapid development of technology has led to the emergence of innovative teaching approaches, such as high-fidelity patient simulation (HFPS). HFPSs have been shown to significantly enhance students' decision-making and intellectual skills. This study aimed to investigate how students learn from the pre-briefing to observation period of the facilitation of the HFPS based on the original quasi-experimental studies. This study analyzed the narratives from 92 students in the intervention group about their learning during the pre-briefing and observation of facilitation. The results indicated that the students learned more independently, received better support and resources for learning, were provided with more appropriate and safe care for the simulated case, and developed higher-level intellectual skills, such as self-directed learning, critical thinking, and clinical reasoning. Importantly, the structured guidelines provided roles and responsibilities and guiding questions or aspects for observation that directed the students to learn more actively and effectively while performing their roles in the HFPS. The structured guidelines serve as a roadmap to instruct students on learning during pre-briefing and applying what they have learned during the facilitation of the HFPS. This roadmap includes the learning journey from novice to competence in knowledge and skills and also from knowing to application. Therefore, this study's results have contributed important knowledge about well-structured HFPS guidelines for all stages of the HFPS, addressing the need for adequate guidance and learning support during the pre-briefing and observation of facilitation. The elements identified during the pre-briefing and observation of facilitation are crucial for directing students to learn and significantly enhance their understanding and application of knowledge and skills, ultimately promoting the development of higher-level intellectual skills, professionalism, and engagement. Nurse educators can incorporate these elements into HFPS training in curricula to enhance students' involvement and optimize the HFPS as an effective teaching tool with structured guidelines providing guidance and support throughout various stages of HFPS training.

Synthetic ground motions in heterogeneous geologies from various sources: the HEMEWS-3D database

Abstract. The ever-improving performances of physics-based simulations and the rapid developments of deep learning are offering new perspectives to study earthquake-induced ground motion. Due to the large amount of data required to train deep neural networks, applications have so far been limited to recorded data or two-dimensional (2D) simulations. To bridge the gap between deep learning and high-fidelity numerical simulations, this work introduces a new database of physics-based earthquake simulations. The HEterogeneous Materials and Elastic Waves with Source variability in 3D (HEMEWS-3D) database comprises 30 000 simulations of elastic wave propagation in 3D geological domains. Each domain is parametrized by a different geological model built from a random arrangement of layers augmented by random fields that represent heterogeneities. Elastic waves originate from a randomly located pointwise source parametrized by a random moment tensor. For each simulation, ground motion is synthesized at the surface by a grid of virtual sensors. The high frequency of waveforms (fmax⁡=5 Hz) allows for extensive analyses of surface ground motion. Existing and foreseen applications range from statistical analyses of the ground motion variability and machine learning methods on geological models to deep-learning-based predictions of ground motion that depend on 3D heterogeneous geologies and source properties. Data are available at https://doi.org/10.57745/LAI6YU (Lehmann, 2023).

Exploring spatial beam shaping in laser powder bed fusion: High-fidelity simulation and in-situ monitoring

Laser beam shaping is a novel and relatively unexplored method for controlling the melt pool conditions during metal additive manufacturing (MAM) processes, but even so it still holds good promise for achieving site-specific tailored properties. In this work, a comprehensive numerical and experimental campaign is carried out to explore this subject within metal laser powder bed fusion (LPBF). More specifically, a multiphysics numerical model is implemented for simulating the heat and fluid flow conditions during LPBF of Ti6Al4V using arbitrary circular beam shapes with various power distributions spanning from a pure Gaussian beam to a pure ring beam profile. The model is subsequently coupled with cellular automata to describe the beam shape effects on the microstructure evolution. Model validation is carried out in a two-fold manner. First, we compare the predicted melt pool cross-section with the one from ex-situ single track experiments, and we find a deviation of less than 9 % in melt pool dimensions. Secondly, advanced in-situ X-ray monitoring is carried out to unravel the melt pool dynamics and we find that the predicted morphology closely matches the in-situ X-ray results. Moreover, it is shown that at lower laser power, a bulge of liquid metal forms at the center of the melt pool when employing ring profiles, and this is ascribed to the absence of recoil pressure at the center of the ring beam. Furthermore, increasing the laser power seems to destabilize the melt pool regime, as the central bulge transforms into a liquid metal jet that periodically collapses and breaks up into hot spatter. Based on the results, we believe that our multiphysics modelling methodology, opens up new pathways for predicting how laser beam shaping influences porosity, surface roughness as well as microstructure formation in LPBF processes.

Jacobian Spheroids, Shallow Encounters, and the Keyhole Map

The study of the dynamics of flybys is featured in several space-mission-related applications, spanning from the design of interplanetary orbits to the computation of impact probabilities with given celestial bodies in planetary protection and defense tasks. While patched-conics, two-body models suffice in accuracy for trajectory planning cases, collision probability assessment requires instead more insightful approaches. High-fidelity simulations have shown impact patterns to happen outside nominally colliding trajectories in terms of two-body analysis, all caused by distant or weak interactions between the given trajectory-celestial body pairs. This work proposes a novel concept of sphere of influence based on the Jacobian of the three-body dynamics, resulting in a wider definition of encounters that encompasses weak interactions up to an arbitrary threshold. The so-defined Jacobian spheroids are then used to construct the Keyhole map, a graphical tool that maps in the orbital elements space both nominal and off-nominal keyholes, i.e., collision paths accounting for three-body weak interactions. The proposed concepts arise from and are compared against the on-ground casualty risk assessment of the European Space Agency’s mission JUICE.

Simulation Training to Increase Holding of Fragile Infants in Cardiac Intensive Care Units.

Promoting bonding and neurodevelopmental care is an important element in the cardiovascular intensive care unit (CICU); however, holding of infants by family members is inconsistently practiced. This quality improvement study aimed to safely increase the holding of medically complex infants in the CICU by developing a holding guideline and offering simulation-based staff education. Using consensus methodology and high-fidelity simulation, an expert work group created a holding guideline and training to increase staff confidence and competence in holding critically ill infants in the CICU. The effectiveness of the intervention was tested via a postintervention survey used to assess participants' confidence in and comfort with holding critically ill infants and elicit suggestions for further support. Participants who engaged with the mannequin were surveyed to assess the validity of the simulation trainer. After the intervention, participants (N = 130) reported increased confidence with infant holding (76%) and greater confidence in preparing families to hold their infants. Participants (95%) strongly agreed that the simulation enabled realistic, safe practice in holding a medically complex infant with catheters and tubes. More years of experience and engagement with the simulation trainer were associated with increased posttraining confidence. Participants cited increased resources, practice, and adherence monitoring as key supports for infant holding. Expert group consensus with high-fidelity simulation training is a feasible, safe, and reliable method for teaching higher risk skills and guiding protocol development.

The status of interprofessional education for healthcare students in South Korea: a scoping review focusing on simulation-based education.

Simulation-based education is gaining attention worldwide as it is recognized as effective in fostering collaborative skills in healthcare students. We conducted a comprehensive review of simulation-based interprofessional education (IPE) to examine the current state of simulation-based IPE. This scoping review systematically analyzed studies on simulation-based IPE in South Korean healthcare education, following established guidelines. Relevant articles were comprehensively searched, and key data on simulation methods, implementations, and educational effectiveness were extracted for analysis. The present study included nine quantitative studies and one mixed-methods study. The majority of participants were undergraduate nursing and medical students. The duration of IPE interventions ranged from 2 hours to 2 weeks. Education methods included standardized patients, high-fidelity simulators, and role-playing. Educational outcomes focused on measuring IPE competencies and satisfaction levels, concentrating on Kirkpatrick levels 1 and 2. While most studies reported high satisfaction levels, there is a need for objective evaluation of educational effectiveness. As simulation-based IPE in Korean healthcare education evolves, there is a need for greater inclusivity of diverse roles, multidisciplinary respect, and scenario development allowing active participation across professions. Establishing institutional frameworks, community linkages, and a deep understanding of IPE's purpose and essence among practitioners is crucial for its academic maturation.

Data-driven prediction of scaling and ignition of inertial confinement fusion experiments

Recent advances in inertial confinement fusion (ICF) at the National Ignition Facility (NIF), including ignition and energy gain, are enabled by a close coupling between experiments and high-fidelity simulations. Neither simulations nor experiments can fully constrain the behavior of ICF implosions on their own, meaning pre- and postshot simulation studies must incorporate experimental data to be reliable. Linking past data with simulations to make predictions for upcoming designs and quantifying the uncertainty in those predictions has been an ongoing challenge in ICF research. We have developed a data-driven approach to prediction and uncertainty quantification that combines large ensembles of simulations with Bayesian inference and deep learning. The approach builds a predictive model for the statistical distribution of key performance parameters, which is jointly informed by past experiments and physics simulations. The prediction distribution captures the impact of experimental uncertainty, expert priors, design changes, and shot-to-shot variations. We have used this new capability to predict a 10× increase in ignition probability between Hybrid-E shots driven with 2.05 MJ compared to 1.9 MJ, and validated our predictions against subsequent experiments. We describe our new Bayesian postshot and prediction capabilities, discuss their application to NIF ignition and validate the results, and finally investigate the impact of data sparsity on our prediction results.

Optimal Agile Satellite Target Scheduling with Learned Dynamics

Target sequencing is an important aspect of agile Earth-observing satellite scheduling. The objective of the problem is to find a feasible imaging sequence that maximizes the cumulative value of unique heterogeneously valued requests; no expendable resource constraints (power and data storage) are considered. Two challenges are encountered: transition times between targets are dictated by dynamics of an arbitrary controller, and the solution space is combinatorial with respect to request count. To find dynamically accurate time-dependent attitude maneuver transition times, a neural network is trained on simulated slew data; this allows for the generation of more physically accurate, and thus better performing, schedules than when using the traditional approach of lower-order models for slew feasibility. Next, slews between requests are represented by a sparsified graph, and a mixed-integer linear program is formulated to solve them; the sparse formulation keeps the problem tractable over longer planning horizons and larger numbers of requests when compared to the standard approach. The solutions are optimal up to the quality of the transition time estimator and a time discretization. Finally, the solutions are verified in a high-fidelity simulation, demonstrating validity when deployed on a lifelike system. Time-dependent request values and multiple satellites are also considered.

Adaptive formulation for probabilistic storm surge predictions through sharing of numerical simulation results across storm advisories

As a tropical storm/cyclone approaches landfall, real-time probabilistic predictions for the anticipated surge provide valuable information for emergency preparedness/response decisions. These probabilistic predictions are made through an uncertainty quantification process that involves: (i) generating a sufficiently large ensemble of storm scenarios based on the nominal storm advisory and the anticipated forecast errors; (ii) performing high-fidelity numerical simulations to obtain surge predictions for each storm scenario; and (iii) estimating surge statistics of interest by assembling the simulation results. This process is repeated whenever the nominal storm advisory is updated. The number of storm scenarios utilized in the analysis directly impacts the statistical accuracy of the probabilistic predictions; a larger ensemble improves accuracy but requires greater computational resources to provide predictions with the desired expediency to guide real-time decisions. This paper revisits two recently proposed Monte-Carlo (MC) frameworks that aim to improve accuracy without increasing the computational burden: adaptive importance sampling (AIS) and adaptive multi-fidelity Monte Carlo (AMFMC). The foundational concept behind them is similar: share numerical simulation results across the probabilistic predictions performed for different storm advisories to accelerate the MC estimation. This is achieved differently for each approach, through adaptive development of an importance sampling proposal density (for AIS) or a surrogate model (for AMFMC). Here, a direct comparison between these frameworks is established, focusing on the mechanisms for the information sharing and the challenges encountered in tuning the algorithm adaptive characteristics to provide probabilistic estimates across a large number of quantities of interest (QoIs), corresponding to the surge predictions for different locations within the coastal region of interest. As this large number results in conflicting choices for the adaptive characteristics, a compromise solution needs to be promoted. The efficacy of the two frameworks is examined in detail in this setting, comparing the accuracy of idealized implementations (adaptive decisions independently made for each QoI) to the accuracy of practical implementations (single, compromise decision within the MC implementation). The study also showcases the importance of information sharing across storm advisories in real-time probabilistic storm surge predictions and provides guidelines for an efficient adaptive MC formulation in such settings.