Simulation Scenarios Research Articles

A Microsimulation Model of Mpox (Monkeypox) in Los Angeles County (LAC): Implications for Future Disease Prevention and Control Strategies among Men Who Have Sex with Men (MSM)

Abstract Background The 2022 mpox outbreak in Los Angeles County (LAC) emphasized the need to prepare for emergent infectious disease outbreaks. Vaccination and promotion of sexual risk reduction practices appeared successful in LAC, as mpox cases declined starting in August. Nonetheless, questions persisted regarding the effectiveness of targeting vaccinations and the role of sexual risk reduction in reducing mpox cases. Methods We collaborated with the LAC Department of Public Health to develop a microsimulation for men who have sex with men (MSM). This model tracked mpox dynamics by age, race/ethnicity, and HIV status, and was calibrated and validated against surveillance data. We simulated counterfactual scenarios to understand the effects of variation in vaccination rates, timing of vaccination roll-out, vaccine allocation, and sexual contact rates. Results In the simulation, doubling the vaccination rate reduced cumulative cases over a 40-week time horizon by 13% but would necessitate 88,995 additional doses. Initiating vaccination two weeks earlier decreased cases by 11%, while an 8-week delay yielded a 20% increase in cases. A three-week earlier decrease in sexual contact rates reduced cumulative cases by 60%, while a three-week delay resulted in a 95% increase. Prioritizing people with HIV (PWH) for vaccination reduced cumulative cases while allocating vaccines to a single racial/ethnic group was not effective. Conclusions Our study highlights the significance of policies to support timely vaccination and sexual partnership reduction to address mpox outbreaks among MSM. These findings also underscore the need to target vulnerable risk groups, such as PWH.

Economic Impact of the Drought in Spain: Measurement for the Adoption of Measures

This paper aims to evaluate the economic implications of meteorological drought in Spain. The study seeks to provide decision-makers with crucial insights into the macroeconomic effects of drought, enabling them to devise mitigation strategies and minimize its impact on economic activity. The Partial Hypothetical Extraction Method (HEM) is employed within the Input-Output analysis framework extended to a Social Accounting Matrix (SAM) of Spain to achieve this goal. The database utilized for this analysis is the FNAM for Spain in 2017, in millions of euros, obtained from the Full International and Global Accounts for Research in Input-Output Analysis (FIGARO) project, a collaboration between Eurostat and the European Commission. The study aims to estimate the economic impact of drought on the productive sectors of the Spanish economy in terms of sectoral production and GDP. This involves simulating the partial reduction in value-added resulting from variations in average water productivity per gross value added, based on the drought indicator SPI-24. Three scenarios are generated: (1) drought, (2) moderate drought and (3) severe drought. In quantitative terms, the simulated drought scenarios could lead to a drop in GDP of 0.88% for the drought scenario, 1.61% for the moderate drought scenario, and 1.76% for the severe drought scenario. Additionally, it is important to recognize that water scarcity hampers the social and economic development of cities and regions beyond the results in quantitative terms.

Palliative care competence development through simulation: Excellence & innovation to meet the AACN essentials

BackgroundThe updated American Association of Colleges of Nursing: The Essentials: Core Competencies for Professional Nursing Education (2021) outlines competence requirements for entry-to-practice nurses. Hospice and palliative care (PC) are identified as one of four spheres of care, emphasizing that nursing knowledge and proficiency in primary PC are critical for all nurses. PurposeThis article identifies key elements for nursing educators to consider when adopting, adapting, or designing practical primary PC simulations to meet The AACN Essentials. MethodsUsing Healthcare Simulation Standards of Best Practice™ (HSSBP) as a framework, a panel of nursing education researchers identified and compared key elements of four PC simulations from varied evidence-based exemplars across the U.S. ResultsOverarching elements in simulation development include choosing modalities, assessing resources, and simulation logistics, creating opportunities for difficult conversations with interprofessional team engagement, and validating scenarios with expert input. Simulation implementation should include tailored pre-briefing to address psychological safety in caring for the dying patient, piloting scenarios, and maximizing observer/vicarious learner roles. Finally, evaluation is vital for competency assessment, scenario enhancement, and logistical changes.Limitations included variability in PC simulation scenarios, learners/learner level, curricula, and geography across selected exemplars, which may affect the general applicability of these findings. ConclusionsSimulations developed according to HSSBP™ provide effective practical experience in PC, affording active and vicarious learners the knowledge and skills essential for baseline nursing competence.

Assessing Clinical Judgment and AACN Domains in Self-Reflection of Video Recorded High-Fidelity Simulation Scenarios.

Clinical judgment is the hallmark of safe patient care. Nurse educators continue to explore best practices to assist prelicensure nursing students in developing clinical judgment. The 3-part purpose of this study was to identify what students learned about themselves related to clinical judgment after viewing their video-recorded high-fidelity simulation scenarios, to assess if video recording is an effective post-debriefing teaching strategy, and to evaluate if the 2021 American Association of Colleges of Nursing (AACN) Domains can be assessed longitudinally. Qualitative interpretive description study of 37 prelicensure nursing students. Ten codes were derived from 314 excerpts: communication, body language, confidence in self, putting data together, collaboration, leadership, safety, skillfulness, areas of improvement, and growth. Longitudinal data showcased the development of clinical judgment, effectiveness of self-evaluation of video recorded simulations as a teaching strategy, and assessment of 5 AACN Domains.

Analysis of inter-satellite optical wireless communication systems for enhanced data transmission in satellite constellations

This research focuses on the comprehensive performance analysis of inter-satellite optical wireless communication (IsOWC) systems, specifically tailored for high data rate applications in Earth observation satellites. The research employs OptiSystem 21.0 simulation software to model and analyze the IsOWC systems. The simulation scenario is set in a low Earth orbit (LEO) constellation, typical for Earth observation satellites. Various system parameters are systematically varied to assess their impact on performance. Modulation types, such as non-return-to-zero (NRZ) and return-to-zero (RZ) are analyzed for their influence on the quality (Q) factor. The study further explores the effects of different wavelengths, antenna apertures, transmitter output powers, and link distances on the IsOWC system performance. This research provides valuable insights into optimizing IsOWC systems for Earth observation satellites, contributing to the advancement of communication technologies in satellite networks. The research shows that the NRZ modulation consistently outperforms RZ modulation in terms of maximum Q factor and minimum BER across different antenna apertures and power levels. Additionally, shorter wavelengths exhibit improved performance, while larger antenna apertures contribute to enhanced signal quality. The relationship between transmitter power and data rate is also investigated, demonstrating the potential for achieving higher data rates with increased transmitter power. The findings guide the selection of parameters to achieve optimal performance, ensuring efficient data transfer and meeting the demands of contemporary Earth observation missions. This study provides conclusive evidence that the 1350 nm wavelength outperforms the 1550 nm wavelength in reliable and high-data-rate optical wireless communications, offering higher received power, superior signal quality, and lower bit error rates. This makes it a preferred choice for systems in challenging space environments and could enhance future satellite communication architectures.

Exploring differences in patient participation in simulated emergency cases in co-located and distributed rural emergency teams – an observational study with a randomized cross-over design

BackgroundIn northern rural Sweden, telemedicine is used to improve access to healthcare and to provide patient-centered care. In emergency care during on-call hours, video-conference systems are used to connect the physicians to the rest of the team – creating ‘distributed teams’. Patient participation is a core competency for healthcare professionals. Knowledge about how distributed teamwork affects patient participation is missing.The aim was to investigate if and how teamwork affecting patient participation, as well as clinicians’ perceptions regarding shared decision-making differ between co-located and distributed emergency teams.MethodsIn an observational study with a randomized cross-over design, healthcare professionals (n = 51) participated in authentic teams (n = 17) in two scripted simulated emergency scenarios with a standardized patient: one as a co-located team and the other as a distributed team. Team performances were filmed and observed by independent raters using the PIC-ET tool to rate patient participation behavior. The participants individually filled out the Dyadic OPTION questionnaire after the respective scenarios to measure perceptions of shared decision-making. Scores in both instruments were translated to percentage of a maximum score. The observational data between the two settings were compared using linear mixed-effects regression models and the self-reported questionnaire data were compared using one-way ANOVA. Neither the participants nor the observers were blinded to the allocations.ResultsA significant difference in observer rated overall patient participation behavior was found, mean 51.1 (± 11.5) % for the co-located teams vs 44.7 (± 8.6) % for the distributed teams (p = 0.02). In the PIC-ET tool category ‘Sharing power’, the scores decreased from 14.4 (± 12.4) % in the co-located teams to 2 (± 4.4) % in the distributed teams (p = 0.001). Co-located teams scored in mean 60.5% (± 14.4) when self-assessing shared decision-making, vs 55.8% (± 15.1) in the distributed teams (p = 0.03).ConclusionsTeam behavior enabling patient participation was found decreased in distributed teams, especially regarding sharing power with the patient. This finding was also mirrored in the self-assessments of the healthcare professionals. This study highlights the risk of an increased power asymmetry between patients and distributed emergency teams and can serve as a basis for further research, education, and quality improvement.

An investigation of the effect of workload on ship engine room operators using fNIRS

This is an investigation into the Performance Shaping Factors (PSFs) associated with ship engine room operators. This study looks at the effect of workload. There is a large portion of human error associated with marine incidents. Human error may be considered as a result of additional supplementary tasks on top of an accustomed workload. The aim of this study is to evaluate the effect of workload on human performance. To achieve this, a TRANSAS simulator series 5000 was used to replicate a real scenario with the addition of workload as a PSF. Each participant engaged in a fault detection and correction task. 20 participants were used for the workload study; all 20 were trained for 3 h to use the engine room software interface. The participants then completed a 30-min ballasting task. During this interaction, 50% of the participants underwent a simulated scenario where the workload was increased. The other 50% were given a standard task. Functional near-infrared spectroscopy (fNIRS) was used to measure the participant's activation levels, more specifically from the dorsolateral prefrontal cortex (DLPFC) region of the cerebrum. The results showed an increase in activation of the DLPFC during each phase of the task, this trend was magnified by the addition of increased workload. The results are discussed with respect to human performance during varying workload. From the results of this study, a human classification performance model was developed. This model can be used by the maritime industry to better evaluate and understand human error causation.

Mathematical modeling, sensitivity analysis, and optimal control of students awareness in mathematics education

This article presents a continuous-time mathematical model, EFSMK, integrating various educational categories, serving as a valuable tool for understanding students interactions with mathematics. The paper begins by exploring the necessary preliminaries to describe the model. Subsequently, we examine the model’s well-posedness, focusing on the positivity and boundedness of solutions. The basic reproduction number is then derived using the next-generation matrix method. The model exhibits two stable states: the mathematics-free equilibrium point and the learning equilibrium point for students who struggle with mathematics. The study shows that when the basic reproduction number is less than one, the mathematics-free equilibrium point is globally asymptotically stable. Conversely, when the basic reproduction number exceeds one, the learning equilibrium point for students who struggle with mathematics is globally asymptotically stable. Numerical simulations validate the theoretical findings. In the last part of the paper, we present a structured model involving two distinct strategies aimed at improving the mathematical skills of students who encounter difficulties in this subject. The first strategy consists of offering individualized or small-group tutoring sessions, focusing on challenging topics and using alternative teaching methods. The second strategy entails adjusting the overall program and pedagogical approach to better respond to diverse learning needs. This approach uses Pontryagin’s maximum principle and iterative analysis to determine optimal controls. The effectiveness of these strategies is evaluated using numerical simulations in various scenarios.

Optimal agricultural structure allocation based on carbon source/sink accounting

China has implemented policies to promote fuel ethanol development and achieve its dual-carbon goal. Under the policy, the significant modifications will occur in the allocation of land use resources within ethanol raw material cultivation regions. In the process of land use change, ensuring food provision and protecting the regional carbon sink are also crucial for sustainable development. This study explores carbon distribution in the Guiping section of the Yujiang River basin, where bioethanol raw materials are cultivated. Using the CLUE-S model, a multi-objective model prioritizing economic and ecological benefits is constructed. Four scenarios (i.e., the natural expansion scenario (NES); the food conservation scenario (FCS); the bioethanol feedstock conservation scenario (BCS), and the carbon sinks conservation scenario (CCS) are simulated, predicting land resource distribution by 2030. The total carbon sink decreased by 285.60 gC·m−2·a−1, showing a seasonal trend of higher levels in summer and lower levels in winter. In 2030, the total carbon sink decreased in the NES scenario, while it increased in the FCS and BCS scenarios. The CCS scenario had the largest increase, approximately 582.27 × 103 tC. These simulated scenarios consider various economic and environmental factors, providing a scientific basis for the efficient allocation of regional land resources. They also have significant implications for local energy, economic, and environmentally sustainable development, as well as promoting the achievement of carbon neutrality.

Deep causal inference for understanding the impact of meteorological variations on traffic

Understanding the causal impact of meteorological variations on traffic conditions (e.g., traffic flow and speed) is crucial for effective traffic prediction and management, as well as the mitigation of adverse weather effects on traffic. However, many existing studies focused on establishing associations between meteorological situations and traffic, rather than delving into causal relationships, especially with deep learning techniques. Consequently, the ability to identify specific meteorological conditions that significantly contribute to traffic congestion or delays is still limited. To address this issue, this study proposes the Meteorological-Traffic Causal Inference Variational Auto-Encoder Model (MT-CIVAE) to estimate the causal impact of fine-grained meteorological variations (e.g., rain and temperature) on traffic. Specifically, MT-CIVAE is based on the Variational Auto-Encoder and consists of an encoder to recover the distribution of latent confounders and a decoder to estimate the conditional probabilities of treatments. Transformer encoder layers are incorporated to analyze the spatial and temporal correlations of historical traffic data to further enhance the inference capability. To evaluate the effectiveness of the proposed approach for causal inference, real-world traffic flow and speed datasets collected from California, along with corresponding fine-grained meteorological datasets, are employed. The counterfactual analysis is conducted using artificially generated meteorological conditions as treatments, which allows for the simulation of hypothetical meteorological scenarios and the evaluation of their potential impact on traffic conditions. This study develops deep learning methods for assessing the causal impact of meteorological variations on traffic dynamics, offering explanations and insights that can assist transportation institutions in guiding post-meteorology traffic management strategies.

SurvdigitizeR: an algorithm for automated survival curve digitization

BackgroundDecision analytic models and meta-analyses often rely on survival probabilities that are digitized from published Kaplan–Meier (KM) curves. However, manually extracting these probabilities from KM curves is time-consuming, expensive, and error-prone. We developed an efficient and accurate algorithm that automates extraction of survival probabilities from KM curves.MethodsThe automated digitization algorithm processes images from a JPG or PNG format, converts them in their hue, saturation, and lightness scale and uses optical character recognition to detect axis location and labels. It also uses a k-medoids clustering algorithm to separate multiple overlapping curves on the same figure. To validate performance, we generated survival plots form random time-to-event data from a sample size of 25, 50, 150, and 250, 1000 individuals split into 1,2, or 3 treatment arms. We assumed an exponential distribution and applied random censoring. We compared automated digitization and manual digitization performed by well-trained researchers. We calculated the root mean squared error (RMSE) at 100-time points for both methods. The algorithm’s performance was also evaluated by Bland–Altman analysis for the agreement between automated and manual digitization on a real-world set of published KM curves.ResultsThe automated digitizer accurately identified survival probabilities over time in the simulated KM curves. The average RMSE for automated digitization was 0.012, while manual digitization had an average RMSE of 0.014. Its performance was negatively correlated with the number of curves in a figure and the presence of censoring markers. In real-world scenarios, automated digitization and manual digitization showed very close agreement.ConclusionsThe algorithm streamlines the digitization process and requires minimal user input. It effectively digitized KM curves in simulated and real-world scenarios, demonstrating accuracy comparable to conventional manual digitization. The algorithm has been developed as an open-source R package and as a Shiny application and is available on GitHub: https://github.com/Pechli-Lab/SurvdigitizeR and https://pechlilab.shinyapps.io/SurvdigitizeR/.

Development of an improved Bayesian network method for maritime accident safety assessment based on multiscale scenario analysis theory

The strait is infrastructure for connecting ports, ensuring the national economy and human life. Assessing maritime safety is crucial for strait operation and maintenance. Current methods over-rely on statistical analysis of historical data, which lacks scientific algorithms and cannot accurately assess safety timeliness. Therefore, this study proposes an improved Bayesian network (BN) model based on the multiscale scenario analysis theory to process to the maritime safety assessment process. Firstly, the maritime safety assessment model is constructed based on the knowledge element theory (KET) and scenario analysis theory (SAT). Secondly, a heuristic learning algorithm is used to construct the BN model of maritime accidents to define scenario elements and key parameters. Thirdly, a scenario simulation is conducted with the Bohai Strait as a case to assessment its emergency capability in catastrophic accidents. This paper successfully developed a maritime accident safety assessment framework using an improved BN algorithm within a scenario analysis perspective, significantly enhancing the accuracy and reliability of safety assessments. This methodology used in this study can be used a tool suitable for the safety assessment of major infrastructure accidents at the national level and the results obtained can serve as a reference for dynamically revising emergency plans.

Collaborative Virtual Environments (CVEs) in the Preparation of Pre-service Mathematics Teachers

In recent times, teacher preparation programmes have faced significant challenges as many universities transitioned to fully online or hybrid instructional models as a result of the COVID-19 pandemic outbreak. These sudden shifts drastically decreased classroom teaching opportunities, which was a cornerstone of teacher preparation programmes to help preservice teachers learn how to teach effectively. Therefore, considering the importance of visualizations in mathematics education for facilitating teaching, motivating students, and satisfying their desire to comprehend abstract mathematical concepts, this study investigates the barriers to utilizing, and the effective ways of integrating Collaborative Virtual Environments (CVEs) in the preparation of pre-service mathematics teachers. Underpinned by an interpretivist paradigm, the study employed a qualitative research approach and a case study design. Data were collected from twenty-five pre-service mathematics teachers, two mathematics teacher educators, and one department head. For this study, data collected were analysed using thematic analysis, and data trustworthiness was ensured through triangulation of the unit of analysis. Research findings revealed that the use of CVEs in teacher education programs is hampered by several factors, which include resistance to change, technological limits, costs and time constraints, assessment difficulties, equality and access issues, and a lack of training and support. Furthermore, the findings highlighted that for CVEs to be effective in teacher preparation programmes, mathematics educators should encourage the simulation of classroom scenarios, facilitate group discussions and cooperative learning and participate in virtual professional development opportunities.

Antiferromagnetic artificial neuron modeling of the withdrawal reflex.

Replicating neural responses observed in biological systems using artificial neural networks holds significant promise in the fields of medicine and engineering. In this study, we employ ultra-fast artificial neurons based on antiferromagnetic (AFM) spin Hall oscillators to emulate the biological withdrawal reflex responsible for self-preservation against noxious stimuli, such as pain or temperature. As a result of utilizing the dynamics of AFM neurons, we are able to construct an artificial neural network that can mimic the functionality and organization of the biological neural network responsible for this reflex. The unique features of AFM neurons, such as inhibition that stems from an effective AFM inertia, allow for the creation of biologically realistic neural network components, like the interneurons in the spinal cord and antagonist motor neurons. To showcase the effectiveness of AFM neuron modeling, we conduct simulations of various scenarios that define the withdrawal reflex, including responses to both weak and strong sensory stimuli, as well as voluntary suppression of the reflex.

Efficient and scalable covariate drift detection in machine learning systems with serverless computing

As machine learning models are increasingly deployed in production, robust monitoring and detection of concept and covariate drift become critical. This paper addresses the gap in the widespread adoption of drift detection techniques by proposing a serverless-based approach for batch covariate drift detection in ML systems. Leveraging the open-source OSCAR framework and the open-source Frouros drift detection library, we develop a set of services that enable parallel execution of two key components: the ML inference pipeline and the batch covariate drift detection pipeline. To this end, our proposal takes advantage of the elasticity and efficiency of serverless computing for ML pipelines, including scalability, cost-effectiveness, and seamless integration with existing infrastructure. We evaluate this approach through an edge ML use case, showcasing its operation on a simulated batch covariate drift scenario. Our research highlights the importance of integrating drift detection as a fundamental requirement in developing robust and trustworthy AI systems and encourages the adoption of these techniques in ML deployment pipelines. In this way, organizations can proactively identify and mitigate the adverse effects of covariate drift while capitalizing on the benefits offered by serverless computing.

Flexible Bayesian estimation of incubation times.

The incubation period is of paramount importance in infectious disease epidemiology as it informs about the transmission potential of a pathogenic organism and helps to plan public health strategies to keep an epidemic outbreak under control. Estimation of the incubation period distribution from reported exposure times and symptom onset times is challenging as the underlying data is coarse. We develop a new Bayesian methodology using Laplacian-P-splines that provides a semi-parametric estimation of the incubation density based on a Langevinized Gibbs sampler. A finite mixture density smoother informs a set of parametric distributions via moment matching and an information criterion arbitrates between competing candidates. Algorithms underlying our method find a natural nest within the EpiLPS package, which has been extended to cover estimation of incubation times. Various simulation scenarios accounting for different levels of data coarseness are considered with encouraging results. Applications to real data on COVID-19, MERS and Mpox reveal results that are in alignment with what has been obtained in recent studies. The proposed flexible approach is an interesting alternative to classic Bayesian parametric methods for estimation of the incubation distribution.

Cadmium accumulation in farmlands through irrigation: evidence from multimedia fate modeling and scenario simulations

The studies of heavy metal input and output fluxes from farmland soils will contribute to sustainable agricultural practices. However, debate on the primary pollution to crops has not resulted in the emergence of practical remediation strategies. Hence, the flux should be identified with precision for targeted pollution control. In this study, a catchment downstream of a metal mining area was selected for investigation of Cadmium pollution in the topsoil of farmlands, including the input and output fluxes in different environmental media. The results showed that irrigation was the main input source of Cd to soil, accounting for more than 75%, in which the contribution of dissolved Cd in water was 1–2 orders of magnitude higher than that of Cd adsorbed on suspended particles. Crop harvesting, surface runoff, and leaching were the main Cd output pathways from this area. The dynamic prediction results under different scenarios showed that Cd in farmland soil would be in a cumulative state in the following years by 2050. Straw removing could significantly reduce Cd accumulation in farmland soil. In addition, the substantial increase of crop yield in the future would increase the Cd output flux. This work revealed the accumulation pattern of Cd in farmland soils, and will aid in the development of remediation strategies for cadmium contamination of agricultural soils.

Distributed multi-robot potential-field-based exploration with submap-based mapping and noise-augmented strategy

Multi-robot collaboration has become a needed component in unknown environment exploration due to its ability to accomplish various challenging situations. Potential-field-based methods are widely used for autonomous exploration because of their high efficiency and low travel cost. However, exploration speed and collaboration ability are still challenging topics. Therefore, we propose a Distributed Multi-Robot Potential-Field-Based Exploration (DMPF-Explore). In particular, we first present a Distributed Submap-Based Multi-Robot Collaborative Mapping Method (DSMC-Map), which can efficiently estimate the robot trajectories and construct the global map by merging the local maps from each robot. Second, we introduce a Potential-Field-Based Exploration Strategy Augmented with Modified Wave-Front Distance and Colored Noises (MWF-CN), in which the accessible frontier neighborhood is extended, and the colored noise provokes the enhancement of exploration performance. The proposed exploration method is deployed for simulation and real-world scenarios. The results show that our approach outperforms the existing ones regarding exploration speed and collaboration ability.

A CFD-based compartmental modelling approach for long-term dynamic simulation of water resource recovery facilities

ABSTRACT This article presents a methodology for compartmental model (CM) creation for long-term simulation of water resource recovery facilities (WRRFs). CMs are often focused on reproducing with a lower computational cost, previously simulated scenarios. In contrast, the methodology presented here can represent variable hydraulic conditions, based on the interpolation of data gathered from a set of computational fluid dynamics (CFD) simulations that reproduce representative hydraulic scenarios. This is achieved by modelling with bidirectional flows the exchange flows between fixed compartments, which are defined based on the geometry of the reactors. The resultant hydraulic surrogate model can be implemented in commercial water treatment software to solve biochemical kinetics. The methodology was applied to simulate in WEST®-DHI a WRRF in Vila-Real, Spain. In this contribution, the CM was validated with real plant data. The developed CM provided a quick response simulation with a high level of hydraulic and biochemical detail. This allowed observation of a spatial distribution of component concentration, which could help with sensor location or plant optimisation. The methodology presented here could also be a useful enabler of digital twins to be implemented in WRRF.

Design and Flight Simulation Verification of the Dragonfly eVTOL Aircraft

Recently, electric vertical take-off and landing (eVTOL) aircraft have become a top priority for urban air transportation due to their ability to overcome urban ground traffic congestion. In this research, a new type of scaled lift–cruise ‘Dragonfly’ has been designed. The ‘Dragonfly’ combines the characteristics of an octocopter and a fixed-wing aircraft. Compared with the same type of eVTOL aircraft, it has a longer wingspan and a more stable aircraft structure, it can not only take off and land vertically without the need for a runway, but also fly quickly in a straight line and hover in mid-air. In order to ensure the success of the flight test, it was also simulated in this paper. A simulation scenario highly fitting with the flight test environment of eVTOL is designed in the Gazebo simulation platform, and then combined with the PX4 flight control platform, the system SITL of the constructed aircraft simulation model is carried out on the Gazebo platform, Finally, simulation flight test data for accurate analysis are obtained, the accuracy and stability of the control algorithm are fed back, and scientific support for the follow-up ‘Dragonfly’ aircraft hardware-in-the-loop simulation and physical flight test is provided.