Simulation Engine Research Articles

A localized subdomain smoothing MMALE particle method for efficient modeling FSI problems

Fluid–structure-interaction (FSI) phenomena with multi-phase flow dynamics and structural damage commonly exist in engineering practice, which however bring great challenges to nowadays numerical FSI algorithms. A novel localized subdomain smoothing MMALE particle method (ls-ALEPM) is proposed in this paper for efficient and accurate simulations of large scale FSI problems. The MMALE method and the MPM are strongly coupled by immersing the MPM particles into the MMALE grid. In order to avoid the spurious strain induced by the mixed FSI velocity field, a decoupled stress updating scheme is proposed to update the stress of solid particles by introducing a virtual velocity field in the vicinity of FSI interface. And specifically, the highly accurate polyhedron intersection based method is employed for its remapping phase, which however is time-consuming. Thus, the localized subdomain smoothing method (LSSM) is put forward to accelerate the remapping phase which only involves the distorted regions of computational grid. The LSSM is composed of a distorted subdomain determination step and a combinated mesh smoothing step. Each iteration of the combinated mesh smoothing step consists of the modified GETMe and the weighted average method, and the transfinite interpolation method is adopted if the quality criteria is still not satisfied after prescribed maximum number of iterations. The validity and efficiency of ls-ALEPM is verified by several benchmark numerical examples and practical engineering simulations.

Study on the Effect of Parameter Sensitivity on Engine Optimization Results

The effects of six control parameters, intake valve opening timing (IVO), exhaust valve opening timing (EVO), compression ratio (CR), engine speed, intake temperature, and intake pressure on engine output power, indicated specific fuel consumption (ISFC), and nitrogen oxides (NOx) emissions, are analyzed through engine simulation. The six parameters were categorized into two groups based on the degree of influence: high influence (EVO, speed and intake pressure) and low influence (CR, IVO and intake temperature). The relationship between these two groups of parameters and power, ISFC and NOx emissions was explored. Optimization was carried out for each of the two groups of parameters, and the optimization of the high impact parameters resulted in a higher diversity and wider distribution of the solution set. On the other hand, the optimization of the low-impact parameters resulted in a more concentrated distribution of the solution set, while better reflecting the trade-off between the optimization objectives. For the optimal solutions for both sets of parameters, the high-impact parameters provided significant optimization performance compared to the standard operating conditions. Although power and ISFC were optimized, the optimal solution for the low-impact parameter performed poorly with a significant increase in NOx emissions. Therefore, the parameters should be evaluated for optimization using high impact parameters to improve engine performance.

DeepCPG Policies for Robot Locomotion

Central Pattern Generators (CPGs) form the neural basis of the observed rhythmic behaviors for locomotion in legged animals. The CPG dynamics organized into networks allow the emergence of complex locomotor behaviors. In this work, we take this inspiration for developing walking behaviors in multi-legged robots. We present novel DeepCPG policies that embed CPGs as a layer in a larger neural network and facilitate end-to-end learning of locomotion behaviors in deep reinforcement learning (DRL) setup. We demonstrate the effectiveness of this approach on physics engine-based insectoid robots. We show that, compared to traditional approaches, DeepCPG policies allow sample-efficient end-to-end learning of effective locomotion strategies even in the case of high-dimensional sensor spaces (vision). We scale the DeepCPG policies using a modular robot configuration and multi-agent DRL. Our results suggest that gradual complexification with embedded priors of these policies in a modular fashion could achieve non-trivial sensor and motor integration on a robot platform. These results also indicate the efficacy of bootstrapping more complex intelligent systems from simpler ones based on biological principles. Finally, we present the experimental results for a proof-of-concept insectoid robot system for which DeepCPG learned policies initially using the simulation engine and these were afterwards transferred to real-world robots without any additional fine-tuning.

Design of Low Subsonic Wind Tunnel with Open Return System for Testing Wind Turbines at Low Airspeeds

The The Republic of Indonesia is rich in potential for renewable energy, including abundant wind energy. This study aims to design a subsonic open return wind tunnel for testing wind turbines at low airspeeds. The testing focus includes the evaluation of blade efficiency, bearing performance, and other aspects. Testing at low airspeeds (<5 m/s) is highly relevant to the wind conditions in Indonesia. The design process utilizes Computer Aided Design (CAD), while data collection and analysis are conducted through Computer Aided Engineering (CAE) simulations and theoretical calculations. The wind tunnel comprises components such as contraction, honeycomb, test section, diffuser, and support structure. Airflow over the turbine blades can be observed using smoke visualization in the test section. This research is expected to provide practical contributions to the development of low-speed wind turbines in Indonesia.

The Design and Simulation of Engine Rotating Speed Modulation of Circuit

Since the amplitude and frequency of engine rotating speed signals are changed with the engine rotating speed, data cannot be directly collected due to the high-frequency noise. In this work, an engine rotating speed modulation of the circuit was designed. It was used to retain the complete information of positive and negative periods in signals and convert rotating speed signals to rectangular wave signals with certain amplitude at the same frequency, which could be directly collected. Further, in order to investigate the applicability of our design, a simulation test on the developed circuit based on Multisim was performed. This indicates that the raw signal with high-frequency interference, different amplitudes, and frequencies could be accurately and efficiently converted to regular rectangular wave signals. The design is characterized by a compact structure, low difficulty in device selection, high stability, and low power consumption, which is of extreme significance for accurate rotating speed data collection.

Efficient method for finding nearest neighbors in flocking behaviors using k-dimensional trees

<p>Flocking is a behavior where a group of objects travel, move or collaborate together. By learning more about flocking behavior, we might be able to apply this knowledge in different contexts such as computer graphics, games, and education. A key steppingstone for understanding flocking behavior is to be able to simulate it. However, simulating behaviors of large numbers of objects is highly compute-intensive task because of the n-squared complexity of nearest neighbor for separating n objects. The work in this paper presents an efficient nearest neighbor method based on the k-dimensional trees (KD trees). To evaluate the proposed approach, we apply it using Unity-3D game engine, together with other conventional nearest neighbor methods. The Unity-3D game simulation engine allows users to utilize interaction design tools for programming and animating flocking behaviors. Results showed that the proposed approach outperform other conventional nearest neighbor approaches. The proposed approach can be used to enhance digital games quality and simulations.</p>

Historic buildings to Positive Energy Buildings: a trilemma between energy efficiency, lifecycle carbon and architecture conservation.

Heritage buildings define the distinctive character of many urban areas by creating continuity with the past and serving as a visual cultural reference. To ensure their continued use, these buildings must be adapted for better performance that demands energy retrofitting strategies while satisfying indoor environmental quality. Energy retrofit reduces CO2eq emissions in the operation phase but typically entails embodied carbon and is not always applicable to historic buildings that require architectural and cultural conservation. This study phrases it as a trilemma and aims to identify an optimal balance between increased energy efficiency and decreased lifecycle carbon when architecture conservation is acknowledged. The research is applied to a traditional fisherman’s cottage in the village on Howth Head, Ireland with an aim to make it a Positive Energy Building (PEB). First, the construction method and heritage value are investigated followed by an experimental U-value calculation to identify the intervention needs. Then, a range of retrofit strategies to increase the energy efficiency of the building, but sensitive to the architectural conservation agenda, are proposed. Furthermore, using the EnergyPlus simulation engine, the study analyses the implications of different building materials for thermal insulation, building façade and window retrofit, on the building’s operational energy performance. Finally, for each scenario, a lifecycle carbon analysis was applied to identify the optimal strategy when architecture conservation is considered. The best retrofit strategies for reducing operational carbon do not always decrease embodied carbon. Similarly, the optimal materials to be used to achieve the highest level of energy efficiency and reduced life cycle carbon to become a PEB do not adhere to architectural conservation objectives. Further analysis is undertaken to achieve a balanced strategy which can be adopted in similar conservation retrofit projects.

Modeling and Simulation to Inform Potential New Strategies for BACEi in Treatment of Alzheimers Disease

AbstractBackgroundThe β‐secretase‐1 inhibitors (BACEi), including verubecestat, were extensively studied in prodromal to moderate AD and demonstrated early cognitive decline at doses achieving >50% inhibition of amyloid production. Questions remain as to whether BACEi may still have utility, if used earlier in disease, in combination with other therapies, and/or at lower doses. Modeling and simulation approaches were used to maximize learnings from the prior trials and to make predictions around potential alternative therapeutic approaches.MethodTwo models were developed: 1) A joint plaque turnover model, linking drug exposure with amyloid plaque load (PET data in centiloids), to describe natural progression and treatment response to BACEi and amyloid mAb therapies. All plaque data from natural progression (control arms), BACEi and amyloid mAb treatment arms were well described by a joint model, after accounting for differences in baseline plaque load, dosing and titration regimens, and mechanism of action. 2) Disease progression models for the clinical efficacy outcomes (ADAS‐cog11, CDR‐SB) were developed using a methodology similar to Ref. Evidence for BACEi drug effects (on progression rate and/or early detrimental offset) was tested and dependencies on dose, drug exposure and patient characteristics were explored.ResultThe joint plaque turnover model well described natural progression and treatment response to BACEi and amyloid mAb and allows for prediction of alternative regimens and therapeutic approaches. Simulations indicate that 20‐30% inhibition of BACE would be sufficient to stabilize plaque loads in prodromal patients. Disease progression results for ADAS‐cog11 and CDR‐SB identified a verubecestat drug effect of early detrimental offset with no effect on rate of disease progression supported. The magnitude of the early detrimental offset was small relative to the overall progression seen over 18‐24 months and results suggested that this effect may be reduced in advanced AD patients. A simulation engine utilizing the progression model allows for estimation of sample size needed to check for the detrimental effect in future trials.ConclusionModel‐based simulations can help inform future BACEi therapeutic strategies and trial designs. Reference: Clin Pharmacol Ther. 2015 Mar;97(3):210‐4

Experimental Investigations and Quasi Dimensional Modelling of Spark Ignition Engine Fuelled With Gasoline and Oxyhydrogen Gas Mixture

Oxy-hydrogen gas is a mixture of hydrogen and oxygen gas in 2:1 molar proportion. Oxy-hydrogen gas can be easily and economically produced from electrolysis of water by using dry cell electrolysers. Oxy-hydrogen gas is one of the alternative fuel options which can replace conventional fossil fuels like gasoline, diesel, etc., because of its high-octane number (>130) and moderate calorific value (21.4 MJ/kg). Current research work consists of both experimental and simulation parts. Experiments focussed on studying the performance and emission characteristics of spark ignition engine fuelled with gasoline (petrol) and dual fuel (gasoline + Oxy-hydrogen) mixture at variable load and constant speed condition with three different oxy-hydrogen flow rates. Comparative findings suggest that NOx emission and brake thermal efficiency increased by 1.75% and 800 PPM, respectively, at full load and at a flow rate of 1.54 lit/min. Carbon monoxide emission dropped by 0.25% at full load and at a flow rate of 1.54 lit/min. A two-zonequasi dimensional modelling of spark ignition engine was carried out in order to understand the combustion process efficiently. The predicted values accord well with the experimental data, with just a small variance. Commercially available software MATLAB R2021a is used to develop code for the engine simulation.

Numerical simulation of the diesel engine starting considering air pre-heating in the intake system

BACKGROUND: Low temperature of the intake air is one of the reasons of difficult starting of a cold diesel engine in conditions of negative outside temperatures. For a reliable staring of a cold diesel engine, it is necessary to have the temperature at the end of the compression stroke higher than the fuel self-ignition temperature. As the temperature at the end of the compression stroke is defined, first, by the temperature at the end of the intake stroke, the mentioned condition can be satisfied with preliminary warming of the air entering in a cylinder. AIMS: The article is devoted to solving the task related to development of a mathematical model of a diesel engine taking into account air pre-heating in the intake system and ensuring simulation of the pre-starting mode, starting and operation of a diesel engine in conditions of low negative outside temperature. METHODS: The enhanced mathematical model of a diesel engine basing on heat mechanics (thermodynamics of opened systems) which reflects main features of an internal combustion engine (ICE) as the system transforming energy in time domain is proposed in the article. The mathematical model’s equation system is based on laws of conservation of energy and mass, solid bodies’ motion equation and includes differential equations of changing of temperature and density of the working body in a cylinder, in the intake system and in the ICE’s crankcase, the ideal gas state equation as well as differential equations of changing of rotation velocity and rotation angle of an engine’s crankshaft. RESULTS: The mathematical model was tried out on the example of the 1Ch9.5/8.0 multi-purposed air-cooled diesel engine. The results of simulation of the pre-starting mode, starting and operation of the diesel engine taking into account air pre-heating in the intake system are presented in the article. CONCLUSIONS: Implementation of air pre-heating at the intake in the design of diesel engines is the most effective way of ensure starting of the engines at low negative outside temperature. The obtained results help to develop recommendations of choosing power and operation modes of pre-heating devices for ensuring starting and operation of diesel engines in conditions of low negative outside temperatures.

Forest digital twin: A new tool for forest management practices based on Spatio-Temporal Data, 3D simulation Engine, and intelligent interactive environment

Existing forest digitization studies focus on one-way forest management practice visualization simulation, lacking decision-making feedback and virtual-real interaction synchronization. This paper presents the vision of the forest digital twin paradigm. We construct a forest digital twin to explore a new digital carrier of forest resources using remote sensing data, forest inventory data, the Cesium Digital Earth Engine, forest planning theory and parametric 3D modeling technology. The two-way interaction and thinning experiments showed that the forest digital twin could provide a novel pattern for in-depth analysis of forest spatial structure, individual tree dynamic growth and human-digital twin interaction effects. The successful recognition rate in matching the forest structure seen on real forest structure images with the forest digital scene was 91.3%, indicating that the forest digital twin can characterize the real forest structure significantly. The prediction accuracy of the multi-grade growth model integrating the Bayesian method for DBH, H was more than 90.4%. In addition, ASS-FDT interaction is superior to the assessors (ASS) and forest digital twin (FDT) for stand spatial structure overall optimization. The multi-dimensional stand spatial structure index (F-index) increased by 22.82%. The constructed forest digital twin model shows superior performance in optimizing the stand growth model and enhancing the overall stand spatial structure under the decision-making feedback and real-time interaction strategies. The automatic operation pattern provides a user-friendly forest management practice solution.

The slow and the furious? Performance antipattern detection in Cyber–Physical Systems

Cyber–Physical Systems (CPSs) have gained traction in recent years. A major non-functional quality of CPS is performance since it affects both usability and security. This critical quality attribute depends on the specialized hardware, simulation engines, and environmental factors that characterize the system under analysis. While a large body of research exists on performance issues in general, studies focusing on performance-related issues for CPSs are scarce. The goal of this paper is to build a taxonomy of performance issues in CPSs. To this aim, we present two empirical studies aimed at categorizing common performance issues (Study I) and helping developers detect them (Study II). In the first study, we examined commit messages and code changes in the history of 14 GitHub-hosted open-source CPS projects to identify commits that report and fix self-admitted performance issues. We manually analyzed 2699 commits, labeled them, and grouped the reported performance issues into antipatterns. We detected instances of three previously reported Software Performance Antipatterns (SPAs) for CPSs. Importantly, we also identified new SPAs for CPSs not described earlier in the literature. Furthermore, most performance issues identified in this study fall into two new antipattern categories: Hard Coded Fine Tuning (399 of 646) and Magical Waiting Number (150 of 646). In the second study, we introduce static analysis techniques for automatically detecting these two new antipatterns; we implemented them in a tool called AP-Spotter. We analyzed 9 open-source CPS projects not utilized to build the SPAs taxonomy to benchmark AP-Spotter. Our results show that AP-Spotter achieves 62.04% precision in detecting the antipatterns.

A deep learning based surrogate model for reservoir dynamic performance prediction

Predicting the reservoir dynamic performance is an important basis for exploiting the remaining oil and improving the recovery. Traditional prediction techniques primarily include numerical simulation and reservoir engineering methods, which show technical drawbacks such as low computational efficiency and too simplified assumptions. To tackle this challenge, we propose a surrogate model for reservoir dynamic performance prediction based on a Variety of View deep convolutional encoder-decoder (VoV-DCED) network. The influence of production time is accounted for using dimension expansion. Our model directly predicts the remaining oil saturation distribution with known reservoir permeability maps and varying well schedules. We examine the performance of our model in 2D and 3D waterflooding reservoirs, which shows that the prediction results are in good consistence with the reservoir numerical simulation. The model can be extended to account for the impacts of other factors, such as initial oil saturation distribution, well patterns, and non-frequent heterogeneities. Although deep learning takes a longer time for dataset preparation in comparison to the traditional simulation, the prediction efficiency of our trained model is almost two orders of magnitude higher than that of numerical simulation. Therefore, this surrogate model can act as an efficient tool for automatic history matching and production optimization—which require multiple simulation runs.

Effects of Pre-Injection Strategy on Combustion Characteristics of Ammonia/Diesel Dual-Fuel Compression Ignition Mode

As a zero-carbon clean fuel, the use of ammonia in internal combustion engines is of great significance to achieve the “two-carbon” goal. This paper investigates the effect of the ammonia energy ratio and diesel injection timing on combustion and emissions. Based on Computational fluid dynamics (CFD) and the simulation analysis software, Converge, three-dimensional modeling is carried out for a direct injection diesel engine with a Compression Ignition (CI) mode. Under the initial full-load conditions of 1200 r/min, the engine simulation was calculated. The results show that the peak cylinder pressure increases and then decreases as the ammonia energy ratio increases, the ignition delay time increases, and the CO2 and N2O emissions decrease. With pre-injection, the peak cylinder pressure increases at the same energy ratio and the combustion stage advances, resulting in improved indicated thermal efficiency. In comparison to the pure diesel mode, the pre-injection strategy shows an obvious reduction in greenhouse gas (GHG) emissions with a decrease of 40.9% by adjusting the injection timing, while the single injection strategy shows a reduction of 36.5%. The soot emission peak occurs in the diesel-only mode with 98.13% and 99.6% reductions in emissions under single and pre-injection, respectively. The ammonia–diesel dual-fuel (ADDF) engine with an ammonia-to-energy ratio of 70% and optimized ammonia and diesel injection timing significantly reduces the NH3 emissions and GHG emissions by 69.34%.

Effects of energy-share and ambient oxygen concentration on hydrogen-diesel dual-fuel direct-injection (H2DDI) combustion in compression-ignition conditions

This study investigates the ignition and combustion characteristics of interacting hydrogen (H2) and diesel surrogate jets under simulated compression-ignition engine conditions. The experimental setup includes two converging single-hole injectors in an optically accessible constant-volume combustion chamber (CVCC). The parameters varied in the study are fuel injection durations and ambient O2 concentrations (10 to 21 vol.%). The results show that a longer interaction between the diesel products and the H2 jet is required to achieve ignition of the H2 jet at lower O2 concentrations. Once ignited, the flame stabilises near or at the nozzle, except under the lowest ambient O2 condition of 10 vol.% where a lifted flame is observed. The lift-off response, however, is influenced by the relative injection duration of the fuels, with the interaction between the incoming H2 jet and the diesel combustion recession products possibly playing a role. The interaction between the jets also affects the recorded intensity and the distribution of the diesel fuel jet soot zone.

Research Method for Ship Engine Fault Diagnosis Based on Multi-Head Graph Attention Feature Fusion

At present, there are problems such as low fault data, insufficient labeling information, and poor fault diagnosis in the field of ship engine diagnosis. To address the above problems, this paper proposes a fault diagnosis method based on probabilistic similarity and rank-order similarity of multi-head graph attention neural networks (MPGANN) models. Firstly, the ship engine dataset is used to explore the similarity between the data using the probabilistic similarity of T_SNE and the rank order similarity of Spearman’s correlation coefficient to define the neighbor relationship between the samples, and then the appropriate weights are selected for the early fusion of the two graph structures to fuse the feature information of the two scales. Finally, the graph attention neural networks (GANN) incorporating the multi-head attention mechanism are utilized to complete the fault diagnosis. In this paper, comparative experiments such as graph construction and algorithm performance are carried out based on the simulated ship engine dataset, and the experimental results show that the MPGANN outperforms the comparative methods in terms of accuracy, F1 score, and total elapsed time, with an accuracy rate of 97.58%. The experimental results show that the model proposed in this paper can still fulfill the ship engine fault diagnosis task well under unfavorable conditions such as small samples and insufficient label information, which is of practical significance in the field of intelligent ship cabins and fault diagnosis.

A skeletal chemical reaction mechanism for gasoline-ABE blends combustion in internal combustion engine

Acetone-butanol-ethanol (ABE), serving as the intermediate product in the biobutanol production process, has garnered significant attention as a promising alternative fuel, thanks to its ability to avoid the costs and energy consumption associated with biobutanol purification, as well as its potential to improve thermal efficiency and reduce pollutant emissions of internal combustion engine. However, to date, there has been limited in-depth research into the chemical reaction mechanism for the combustion of gasoline-ABE blends and its application in engine simulations. In this study, a skeletal chemical reaction mechanism of gasoline-ABE blends was constructed, which consists of 87 components and 387 reactions, using a manifold trajectory-based dimension reduction algorithm developed by our research group. The findings illustrate that the skeletal mechanism can accurately predict ignition delay, laminar flame speed, and premixed flame species profiles for each component within gasoline-ABE blends, including isooctane, n-heptane, toluene, acetone, butanol, and ethanol. The skeletal mechanism was subsequently coupled with a CFD model to further validate its efficacy in engine combustion simulation. The results indicate that the combustion characteristics of gasoline-ABE blends in the engine can be reliably reproduced. After conducting a comprehensive simulation analysis, it is revealed that gasoline blended with 30 vol% ABE361(A:B:E = 3:6:1) exhibits a shorter initial combustion duration, an extended main combustion duration, reduced CO emissions, but also increased NOx emissions.

Improved performance of data-driven simulator of liquid rocket engine under varying operating conditions

Integrating a model-based and data-driven approach to generate sufficient number of training data that include time-varying operating conditions is needed to develop a prognostics and health management technique using machine learning for a reusable rocket engine. A data-driven approach was used to predict the engine behavior, which cannot be determined using a model-based approach. This paper proposes a clustered regression model to predict various operating conditions for a reusable rocket engine. The training data were divided in advance according to the operating conditions using the Gaussian mixture model, and multiple regression models were individually trained on each cluster. By switching the regression models according to the operating conditions, the time-series data comprising various operating conditions, such as the combustion and chill-down phases and transition between phases, were accurately predicted when compared with the prediction results of a single regression model. The prediction results of the present hybrid approach agree reasonably well with the static firing test results of a reusable rocket engine if the explanatory variables are adequately included. By integrating model-based and data-driven approaches, adequate training data can be generated for prognostics and health management of the reusable liquid rocket engine.

Modeling, simulation and techno-economic evaluation of a micro-grid system based on steam gasification of organic municipal solid waste

The annual increase in population has led to rapid industrialization, urbanization, and economic growth over the years, becoming the lead cause of increased municipal solid waste (MSW) generation worldwide. In this study, MSW of 50 kg sampled by quartering method from Dandora dumpsite in Nairobi, Kenya is physically characterized and a sample of organic waste taken through proximate and ultimate analyses. Data obtained is used as input to a gasification model developed using advanced simulation and process engineering (Aspen) Plus software to provide characteristics of the yielded syngas. The obtained results are used to design a real gasification system and evaluate its techno-economic performance using HOMER® Pro as the tool for analysis. The techno-economic feasibility of the designed 60 kW gasification system is assessed considering a 165.59 kWh/day community-based load demand, with 11.5 % self-electricity consumption and 88.5 % grid feed. Results show organic and inorganic waste contents of 64.2 % and 35.8 %, respectively. The proximate and ultimate analyses reveal a high volatile matter content of 76.10 %, as well as fixed carbon content of 17.03 % in the biomass- rich-organic MSW. The simulated results show good agreement with experimental data and other simulation findings in literature. The study’s simulation output reveals 61.15 % H2, 1.42 % CH4, 24.12 % CO, 13.30 % CO2 contents following steam gasification at 700 °C. At 100 kg/h waste charge rate, a syngas yield of 2.39 m3/kg, gasification ratio of 2.2705 kg/kg and a lower heating value of 10,150 kJ/Nm3 (24.26 MJ/kg) are obtained. The sensitivity analysis results show increasing trend in CO2 and H2 syngas constituents from 0.4 to 1.0 steam-to-biomass ratio increase at 900 °C constant temperature: CO decrease is observed while CH4 values remain negligible and unaffected. The techno-economic findings reveal that the grid sales serve to offset the system’s annual costs by 39.34 %, obtaining a net present value (NPV) of $1.43 M, levelized cost of electricity (LCoE) of 0.155 $/kWh and 93.5 kg/yr. CO2 carbon footprint. An Organic MSW steam gasification micro grid is thus financially feasible in grid connected mode and is 99 % more carbon neutral compared to the Kenyan grid.

Integrating Battlefield Documentation into Virtual Reality Medical Simulation Training: Virtual Battlefield Assisted Trauma Distributed Observation Kit (BATDOK).

Clinical documentation is an essential component of the provision of medical care, enabling continuity of information across provider and site handoffs. This is particularly important in the combat casualty care setting when a single casualty may be treated by four or more or five completely disparate teams across the roles of care. The Battlefield Assisted Trauma Distributed Observation Kit (BATDOK) is a digital battlefield clinical documentation system developed by the Air Force Research Laboratory to address this need. To support the deployment of this tool, we integrated BATDOK into a commercially available virtual reality (VR) medical simulation platform used by the U.S. Air Force and Defense Health Agency personnel in order to provide an immersive simulation training experience which included battlefield documentation. A multidisciplinary team consisting of medical educators, VR simulation engineers, emergency physicians and pararescuemen, and BATDOK developers first developed a specification for a virtual BATDOK capability, including a detailed listing of learning objectives, critical interfaces and task plans, and sensor integrations. These specifications were then implemented into the commercially available Virtual Advancement of Learning for Operational Readiness VR Medical Simulation System and underwent developmental testing and evaluation during pararescueman training exercises at the Air Force Special Operations Command Special Operations Center for Medical Integration and Development. The BATDOK capability was successfully implemented within the VR Medical Simulation System. The capability consisted of a virtual tablet with replicated interfaces and capabilities based on the developed specifications. These capabilities included integrated point-of-care ultrasound capability, multi-patient management, vitals sign monitoring with sensor pairing and continuous monitoring, mechanism of injury documentation (including injury pattern documentation), intervention logging (including tourniquets, dressing, airways, lines, tubes and drains, splints, fluids, and medications), and event logging. The capability was found to be operational and in alignment with learning objectives and user acceptance goals.