Existing Simulation Models Research Articles

A study on Modeling and Simulation of Sports Training Injury Optimization from a Biomechanical Perspective

With the development of artificial intelligence technology today, sports training is one of the important ways to keep people healthy. Reasonable sports training can keep people happy, and can also improve the physical quality of athletes. However, because the intensity of sports training is greater than the physical endurance, it may lead to physical injury. The sports training injury model can collect the physical data of athletes, and simulate whether the actions that athletes want to practice are standard and correct through the collected data. If there are errors in the simulated exercise actions, the wrong exercise actions can be corrected in time, so as to reduce the incidence of sports injury accidents. The construction of the experimental model and the calculation and analysis process of the construction data are very complex, and the existing simulation models are difficult to achieve this, which has a significant impact on the subsequent development of the simulation model. In view of this phenomenon, this paper, on the basis of the sports training injury model, combined with the neural network method, conducted an effective research on the construction of the sports training injury model, and inspected the performance of the model by testing the accuracy of the evaluation of the injury risk level, the degree of sports training injury, and the accuracy of the evaluation of the prediction results of the sports training injury model. The experimental data shows that the maximum accuracy of the sports training injury model in the horizontal and vertical directions was 94.43% and 95.26% respectively, and the maximum accuracy of the single injury degree and the composite injury degree was 48.68% and 55.01% respectively, which had high accuracy and operational efficiency, and can effectively avoid the occurrence of training injury.

A novel low-cost high-fidelity porcine model of liver metastases for simulation training in robotic parenchyma-preserving liver resection.

In the era of minimally invasive surgery (MIS), parenchyma-preserving liver resections are gaining prominence with the potential to offer improved perioperative outcomes without compromising oncological safety. The surgeon learning curve remains challenging, and simulation plays a key role in surgical training. Existing simulation models can be limited by suboptimal fidelity and high cost. We describe a novel, reproducible, high-fidelity, low-cost liver metastases model using porcine livers from adult Landrace pigs, with porcine perinephric fat used to simulate subcapsular metastases. This model was then utilised in a training session for surgical trainees performing robotic parenchyma-preserving surgery (PPS) under the guidance of expert robotic surgeons, with feedback being recorded. Trainees rated the model highly on its fidelity to human liver simulation (median score 9), tissue handling (median score 8), and overall usefulness (median score 9). Tissue handling was felt to simulate in vivo liver resection closely, while suggestions for improvement included adding simulated blood flow. This is a novel, low-cost, high-fidelity simulation model of liver metastases with high acceptability to surgical trainees, which could be readily adopted by other training centres.

Economic impact of inclusion of black soldier fly products in broiler diets: A comparison between conventional and higher animal welfare production systems in the Netherlands

The primary objectives of this study were to analyze economic feasibility of incorporating black soldier fly products in broiler feed and to compare the viability of alive (alive grown larvae (AGL)) or processed (defatted larvae meal (DLM) and insect oil) insect inclusion into the two Dutch broiler welfare systems: conventional and better life one star (BLS).An existing simulation model for insect production was adapted to build an economic model for the insect-fed broiler breast meat supply chain. Amounts, full costs, and breakeven prices of different production setups were compared and the impacts of specific input parameters were tested by sensitivity and breakeven analyses.Results indicated a hypothetical demand of 2.15 million tons wet matter of raw substrate to meet the total Dutch breast meat production (BLS broilers and 10% AGL inclusion). In case of 10% processed insect inclusion, the conventional broiler breast meat price increased from 3,947 to 6,069 €/t (53.7%) and the BLS breast meat price from 6,493 to 9,662 €/t (48.8%). Alive insect inclusion resulted in a smaller cost increase (42.2%) by skipping the costly processing step. To become price competitive, DLM or AGL must cost 618 or 801 €/ton dry matter (tDM), respectively, requiring a raw substrate mix price of −109 €/tDM.High input amounts and costs currently hinder the widespread adoption of insect products in Dutch mass poultry feed. However, in the long run, using negative value substrates or targeting niche markets for broiler welfare with low AGL inclusion levels could become viable business models.Research is needed on the impact of negative value substrates on insect production performance and contamination risks. Furthermore, the impact of varying AGL composition requires special attention.

Wear load analysis of conical cutter crushing rock based on SPH to SPH interaction method

Wear is the main failure type for conical cutters. The main load causing its wear is difficult to measure in the test, and the existing simulation models cannot simulate the wear load. Thus, the three-dimensional dynamic model for conical cutter crushing rock is established based on the SPH-SPH interaction method to analyze the relationship between the cutting performance factors and cutter wear. The factors are taken into account such as the stress distribution of rock, kinetic energy and internal energy and displacement of rock, cutting load of cutter, stress of cutter. Then, it is studied that the effect of cutting angle on the cutting performance parameters such as specific energy consumption, rock avalanching, cutting load, load fluctuation, cutter tip stress and cutter side stress. Finally, the cutting angle selection reference of the cutter is given under different consideration factors.

Simulation methods for local microstructure evolution-cooling and time–temperature transformation behavior in heat treatment of tool steels

This paper presents the development of a simulative workflow capable of predicting microstructural evolution during heat treatment processes. It represents a meaningful advance in this field by extending existing simulation models previously published by the authors. In this previous work, the software solutions MatCalc®, MATLAB®, and Abaqus FEA® were coupled to calculate several local microstructural properties: the carbide content, the type, the distribution, and the chemical composition of the matrix. In addition, the model could determine the proportions of microstructural components such as martensite and retained austenite within the matrix. The hardening treatment was simplified by assuming a fast quenching, leading to complete martensitic phase transformation. However, this assumption may not be valid for larger components, leaving room for optimization. Therefore, the simulation model in this publication has been successfully extended to include local solution-state dependent time–temperature transformation behavior. In addition, an automated microstructure simulation of the entire component is now possible. As an application example, two tool geometries of different sizes were simulated with an identical furnace heat treatment. The same furnace temperature (T = 1050 °C) and the same holding time (t = 60 min) were simulated with a slow air cooling (Tair = 25 °C). The austenitizing temperature and holding time were chosen to dissolve a sufficient amount of carbides during austenitization, and the slow cooling rates were chosen to form diffusion controlled phases such as bainite or pearlite. To validate the simulation model, the simulated time–temperature sequences were reproduced experimentally in a quenching dilatometer. The resulting real microstructures were compared with the simulated ones.

Selecting a priority criterion when forming optimal management of a production enterprise using digitalization tools

Relevance. At the present stage, economic and mathematical methods cannot be fully implemented without the use of digitalization tools. The center of many modern theoretical studies, both by foreign and Russian authors, is a simulation model that allows the most complete reflection of both the theoretical message under consideration and its nuances, to which a software package or modeling tools can be fully adapted. The activities of various production and technology companies may be influenced by external factors, which must be resolved not only strategically, but also operationally. Methodological problems lie in the choice of a priority criterion for the formation of optimal management of a manufacturing enterprise using digitalization tools.The purpose is to select a priority criterion for the formation of optimal management of a manufacturing enterprise using digitalization tools.Objectives. objective prerequisites for the construction of simulation models based on an optimization approach are given; the initial formulation of the convex programming problem is justified, an analysis of the priority of weight coefficients is given; the accuracy of calculation and the type of utility function is justified when the analysis of the prevailing normalized prices is assumed. In such initial conditions, the solution of the convex programming problem can be reduced to finding the optimum in the production planning problem.Methodology. The study was carried out using methods of logical and comparative analysis of existing simulation models of operational management of the company, as well as methods of mathematical modeling.Results. The paper proposes an algorithm for the phased implementation of the optimization task, taking into account three relevant responses of the system to the use of resource-saving technologies, determined by the use of a specific type of technological production.Conclusions. The priority of choosing a criterion in the formation of optimal management of a manufacturing enterprise using digitalization tools creates favorable conditions for the system's response to technology changes, depending on three possible states of production technology: ultra-efficient technology, extremely backward technology, medium or normal technology.

Availability simulation and transfer prediction for bike sharing systems based on discrete event simulation

To improve the operational efficiency of Bike Sharing Systems (BSSs), predicting users’ real demand plays an essential role. Previous demand predictions have heavily relied on the historical rides (explicit demand) recorded in the BSS management information system. Since there are no bikes available for rent, users' unfulfilled ride expectations (implicit demand) cannot be captured in the system. This paper presents an approach for simulating station-level availability and predicting transfers for BSS. Firstly, the (s,S) inventory strategy combined with an iterative optimization-seeking algorithm to improve the existing simulation model. Then, the K-means method is used to cluster the similarity among stations. The transfer prediction uses source data to aid the learning of target data quickly. Finally, a real case study is conducted to validate the practicability. The simulation experiment indicates that it is possible to estimate the maximum percentage of underestimation of real demand at a station. Through regression analysis, this paper investigates the complex nonlinear relationship between the percentage underestimation of real demand and the easily accessible implicit demand influencing factors. In practical applications, managers can use this method to optimize the number of bikes placed for BSSs.

A comprehensive phenomenological model for unburned fuel emission simulation applied to natural gas engines

The development of lean-burn concepts for internal combustion engines, utilizing a homogeneous air/fuel charge, aims to improve multiple aspects simultaneously, including thermal efficiency, fuel consumption, nitric oxide, and carbon monoxide emissions. However, the adoption of this technology may result in a notable increase in the emission of unburned hydrocarbons (uHC) compared to stoichiometric engines. The sources of uHC are diverse and their relative significance depends on fuel characteristics, engine operating conditions, and geometric features of the combustion chamber. This concern becomes more pronounced when considering engines fueled with natural gas, as methane has a high global warming potential compared to other pollutant emissions. This research proposes an enhancement to an existing simulation model that describes the primary mechanisms involved in uHC formation, which are filling/emptying of the piston top-land crevice volume and the flame wall quenching, with subsequent uHC post-oxidation. The improvements include the calculation of flame penetration into crevice volumes, the description of uHC production from non-piston crevices, and the introduction of a novel scavenging model. The uHC model is integrated into commercial software (GT-Power) as a “user routine”. Validation of the model is conducted with reference to three large bore engines (W31DF, W31SG, and W46SG) with bore sizes of 31 cm and 46 cm, fueled with natural gas, and operated under lean mixtures (λ > 2), employing different ignition methods (dual fuel mode or pre-chamber device). The uHC model is validated against engine-out measurements, exhibiting an average error of 10.0 % for W31DF, 19.4 % for W31SG, and 14.0 % for W46SG engines, respectively. The reliability of the uHC model is demonstrated by the absence of case-specific adjustments to its tuning constants, even when there are significant variations in engine load and geometric design of the crevice volumes.

Research of time lag characteristics of Pilot-operated Solenoid Valve Damper based on multi-physics field

The development of semi-active suspensions has introduced Pilot-operated Solenoid Valve Dampers (PSVD) that can adjust damping characteristics for different road conditions while considering stability and comfort. However, the PSVD’s additional control system and valve-controlled components make it challenging to avoid time lag in the response of the damping force when the control current signal is switched. This time lag characteristic significantly impacts the performance of the control system, making it difficult to achieve optimal dynamic performance and potentially compromising the safety of drivers and passengers. As a result, understanding and representing the PSVD time lag characteristics and investigating their influence have become important research areas in the field of semi-active suspension. This article begins by explaining the mechanism behind the generation of PSVD time lag characteristics. It analyzes the structure and operating principle of the PSVD, identifying two main types of time lag: electromagnetic time lag and inertial time lag. To address the limitations of existing simulation models, the study combines parameterization and finite element simulation to create a multi-physics field time lag characteristics kinetic representation model of the PSVD, incorporating the electric, magnetic, mechanical, and fluid aspects. To validate the accuracy of the time lag characteristics simulation model, tests on the velocity and time lag characteristics of the PSVD are conducted. The simulation results are compared to the test results, demonstrating that the maximum error of the lag time meets the engineering confidence requirement. This confirms the feasibility of establishing a simulation model for the PSVD time lag characteristics. Finally, we analyze the PSVD total lag time under different working conditions using the simulation model. It explores the relationship between the PSVD total lag time and the lag time of each component, proposes a lag time decomposition relationship, and investigates the crucial influencing factors on the lag time.

Gaussian process emulation to improve efficiency of computationally intensive multidisease models: a practical tutorial with adaptable R code

BackgroundThe rapidly growing burden of non-communicable diseases (NCDs) among people living with HIV in sub-Saharan Africa (SSA) has expanded the number of multidisease models predicting future care needs and health system priorities. Usefulness of these models depends on their ability to replicate real-life data and be readily understood and applied by public health decision-makers; yet existing simulation models of HIV comorbidities are computationally expensive and require large numbers of parameters and long run times, which hinders their utility in resource-constrained settings.MethodsWe present a novel, user-friendly emulator that can efficiently approximate complex simulators of long-term HIV and NCD outcomes in Africa. We describe how to implement the emulator via a tutorial based on publicly available data from Kenya. Emulator parameters relating to incidence and prevalence of HIV, hypertension and depression were derived from our own agent-based simulation model and other published literature. Gaussian processes were used to fit the emulator to simulator estimates, assuming presence of noise for design points. Bayesian posterior predictive checks and leave-one-out cross validation confirmed the emulator’s descriptive accuracy.ResultsIn this example, our emulator resulted in a 13-fold (95% Confidence Interval (CI): 8–22) improvement in computing time compared to that of more complex chronic disease simulation models. One emulator run took 3.00 seconds (95% CI: 1.65–5.28) on a 64-bit operating system laptop with 8.00 gigabytes (GB) of Random Access Memory (RAM), compared to > 11 hours for 1000 simulator runs on a high-performance computing cluster with 1500 GBs of RAM. Pareto k estimates were < 0.70 for all emulations, which demonstrates sufficient predictive accuracy of the emulator.ConclusionsThe emulator presented in this tutorial offers a practical and flexible modelling tool that can help inform health policy-making in countries with a generalized HIV epidemic and growing NCD burden. Future emulator applications could be used to forecast the changing burden of HIV, hypertension and depression over an extended (> 10 year) period, estimate longer-term prevalence of other co-occurring conditions (e.g., postpartum depression among women living with HIV), and project the impact of nationally-prioritized interventions such as national health insurance schemes and differentiated care models.

Strategies and sustainability in fast charging station deployment for electric vehicles.

This comprehensive review investigates the growing adoption of electric vehicles (EVs) as a practical solution for environmental concerns associated with fossil fuel usage in mobility. The increasing demand for EVs underscores the critical importance of establishing efficient, fast-charging infrastructure, especially from the standpoint of the electrical power grid. The review systematically examines the planning strategies and considerations for deploying electric vehicle fast charging stations. It emphasizes their unique dual role as loads and storage units, intricately linked to diverse road and user constraints. Furthermore, the review underscores the significant opportunity surrounding these stations for the integration of distributed renewable energy sources. It thoroughly explores the challenges and opportunities intrinsic to the planning and localization process, providing insights into the complexities associated with these multifaceted stations. Renewable resources, including wind and solar energy, are investigated for their potential in powering these charging stations, with a simultaneous exploration of energy storage systems to minimize environmental impact and boost sustainability. In addition to analyzing planning approaches, the review evaluates existing simulation models and optimization tools employed in designing and operating fast charging stations. The review consolidates key findings and offers recommendations to researchers and grid authorities, addressing critical research gaps arising from the escalating demand for electric vehicle fast-charging infrastructure. This synthesis is a valuable resource for advancing understanding and implementing robust strategies in integrating EVs with the electrical power grid.

Simulation of FES on the forearm with muscle-specific activation resolution.

Functional electrical stimulation (FES) is an established method of supporting neurological rehabilitation. However, particularly on the forearm, it still cannot elicit selective muscle activations that form the basis of complex hand movements. Current research approaches in the context of selective muscle activation often attempt to enable targeted stimulation by increasing the number of electrodes and combining them in electrode arrays. In order to determine the best stimulation positions and settings, manual or semi-automated algorithms are used. This approach is limited due to experimental limitations. The supportive use of simulation studies is well-established, but existing simulation models are not suitable for analyses of selective muscle activation due to missing or arbitrarily arranged innervation zones. This study introduces a new modeling method to design a person-specific digital twin that enables the prediction of muscle activations during FES on the forearm. The designed individual model consists of three parts: an anatomically based 3D volume conductor, a muscle-specific nerve fiber arrangement in various regions of interest (ROIs), and a standard nerve model. All processes were embedded in scripts or macros to enable automated changes to the model and the simulation setup. The experimental evaluation of simulated strength-duration diagrams showed good coincidence. The relative differences of the simulated amplitudes to the mean amplitude of the four experiments were in the same range as the inter-experimental differences, with mean values between 0.005 and 0.045. Based on these results, muscle-specific activation thresholds were determined and integrated into the simulation process. With this modification, simulated force-intensity curves showed good agreement with additionally measured curves. The results show that the model is suitable for simulating realistic muscle-specific activations. Since complex hand movements are physiologically composed of individual, selective muscle activations, it can be assumed that the model is also suitable for simulating these movements. Therefore, this study presents a new and very promising approach for developing new applications and products in the context of the rehabilitation of sensorimotor disorders.

Predicting dynamic stability from static features in power grid models using machine learning.

A reliable supply with electric power is vital for our society. Transmission line failures are among the biggest threats for power grid stability as they may lead to a splitting of the grid into mutual asynchronous fragments. New conceptual methods are needed to assess system stability that complement existing simulation models. In this article, we propose a combination of network science metrics and machine learning models to predict the risk of desynchronization events. Network science provides metrics for essential properties of transmission lines such as their redundancy or centrality. Machine learning models perform inherent feature selection and, thus, reveal key factors that determine network robustness and vulnerability. As a case study, we train and test such models on simulated data from several synthetic test grids. We find that the integrated models are capable of predicting desynchronization events after line failures with an average precision greater than 0.996 when averaging over all datasets. Learning transfer between different datasets is generally possible, at a slight loss of prediction performance. Our results suggest that power grid desynchronization is essentially governed by only a few network metrics that quantify the networks' ability to reroute the flow without creating exceedingly high static line loadings.

A strategy for modelling sediment distribution along slope lengths of an ephemeral gully through the simulated scouring experiments

Accurately describing the soil erosion process is crucial for improving the accuracy of existing simulation models of ephemeral gully erosion. However, a laboratory experimental strategy for simulating sediment distribution along slope lengths of an ephemeral gully is lacking. This limitation greatly hinders the development of simulation models for ephemeral gully erosion. In this study, we constructed six soil flumes with 12 m in length, 0.1 m in width, and 0.5 m in depth, which were filled with cultivated loess soils. Four flow rates (5.33, 10.67, 21.33, and 42.67 × 10−3 m3 s−1 m−1), four slope gradients (5°, 10°, 15°, and 20°), and six gully lengths (2, 4, 6, 8, 10 and 12 m) were adopted to perform a series of laboratory scouring experiments. The measured sediment concentrations at the outlet ranged from 23.39 kg m−3 to 534.60 kg m−3. The trend of exponential increase in sediment concentrations along slope lengths of an ephemeral gully was similar to that observed in a rill. According to the fitting equation, the maximum sediment concentrations (Ae) ranged from 358.05 kg m−3 to 910.88 kg m−3. The attenuation coefficients (Be) indicated the rate of increase in sediment load with slope length, which ranged from 0.042 m−1 to 0.185 m−1. By combining the measured sediment loads along the slope length (8 m) with the sediment transport capacity, the sediment distribution along slope lengths of an ephemeral gully could be accurately quantified. Moreover, the results confirmed that a flume with a short slope length (4/6 m) could effectively determine the sediment distribution along slope lengths at gentle slope of 5° and 10°. However, a minimum length of 8 m was advisable at steep slope of 15° and 20°. In summary, our experimental strategy provides an efficient method for determining the sediment distribution along slope lengths of an ephemeral gully.

Productivity improvement through assembly line balancing by using simulation modeling in case of Abay garment industry Gondar

This study aims to balance the garment line of the polo shirt operation by utilizing line balancing techniques. These techniques are employed to enhance the efficiency and productivity of the Abay garment industry. Observational methods and stopwatch timing were employed to collect data on the processing times for each operation. Subsequently, the standard allowed minute was determined based on work measurement principles. To assess statistical significance and identify appropriate expressions for the existing simulation modeling, all collected data underwent statistical analysis using the Arena input analyzer. Moreover, Arena simulation software was employed in this research to simulate and evaluate the effectiveness of both the current and proposed polo shirt sewing line models. Building upon the existing model, alternative improved models were proposed. The alternative models yielded significant improvements in multiple performance metrics, including output, capacity utilization, waiting time in queues, and number of products waiting. These improvements indicate an increase in productivity resulting from the implementation of balanced production lines. The outcomes of the improved scenario included an increase in output from 288 to 381, a rise in line efficiency from 39.06 % to 55.64 %, and an enhancement in labor productivity from 54.25 % to 66 %. Additionally, the cost of labor was reduced by 15.63 %, while revenue experienced a notable increase of 30 %.

Development of a Simulation Model for Digital Twin of an Oscillating Water Column Wave Power Generator Structure with Ocean Environmental Effect

This research article focuses on developing a baseline digital twin model for a wave power generator structure located in Yongsu-ri, Jeju-do, South Korea. First, this study performs a cause analysis on the discrepancy of the dynamic properties from the real structure and an existing simulation model and finds the necessity of modeling the non-structural masses and the environmental factors. The large amounts of the ballast are modeled in the finite element model to enhance the accuracy of the digital twin. Considering the influence of environmental factors such as tide level and wave direction, the added mass effect of structural members, one of the hydrodynamic effects, depending on the change of the ocean environments is calculated based on the rule of Det Norske Veritas and applied. The results indicate that non-structural mass components significantly impact the dynamic characteristics of the structure. Additionally, environmental factors have a greater effect on the dynamic behavior of the box-type structure compared to lightweight offshore structures.

A machine learning model of liquid bridge force and its application in discrete element method

Developing an accurate and efficient contact model for discrete element method (DEM) simulation that can describe the microscopic dynamics of low moisture content wet granular systems continues to be a major challenge. In this study, a improved energetic approach based on the traditional linear unit discretization method is introduced to solve liquid bridge problems. This improved approach significantly ameliorates the efficiency of constructing the databases while ensuring high data accuracy. Using this method, we generated databases consisting of 163,440,000 and 2043,000 data points for the sphere-sphere and sphere-wall systems, respectively. Machine learning capillary force models (ML-Capillary) are trained using the Catboost algorithm. These models demonstrate excellent performance in predicting capillary force between particles as well as between particles and walls, considering different particle sizes, moisture levels, and contact angles, while neglecting the influence of gravity. The corresponding relative errors are below 1.5% and 2%, respectively, which are lower than existing simulation models. We incorporated the obtained models into the discrete element method and calculated capillary force between particle pairs and simulated wet granular column collapse tests. The simulation results show good agreement with experimental results in terms of parameters such as inter-particle capillary forces and particle column collapse run-out length. This study will provide an accurate and efficient numerical model for simulating low saturation wet granular systems using the discrete element method.

Process model design for positive displacement compressors and their experimental validation: Comparison of Optimal Experimental Design and Machine Learning

Valid simulation models play a critical role in enhancing efficiency in development processes and minimizing experimental efforts. As a result, ensuring accurate predictions through model discrimination, Model Calibration, and Validation (MoCaVal) has become increasingly important and is a necessary step to analyze the behavior and evaluate the effectiveness of systems during their initial design stages. Additionally, to achieve the climate objectives, it is imperative that heat pumps predominantly perform the building sector’s heating. The effectiveness of heat pumps hinges on the performance of the compressor. Therefore, there is a requirement for valid compressor models. However, currently, there is no universal compressor model that applies to all refrigerants and designs. Therefore, every refrigerant or design modification requires the development or adaptation of models. Since creating new models is a time-consuming procedure, automated techniques are advantageous.This paper compares two automated model development methods, Optimal Experimental Design (OED) and Machine Learning (ML), to create valid simulation models within the MoCaVal framework. OED is used to calibrate existing simulation models, while ML is employed to develop new models. Our comparison of the two methods focuses on a fixed-speed scroll compressor of 4 kW nominal power, using R410A and featuring 51 measurement points. Using the Full Factorial Plan (FFP) and the D-Optimal Experimental Design (D-OED), predefined models for mass flow rate, electrical power, isentropic and volumetric efficiency were calibrated. Employing the FFP, Machine Learning (ML) was also applied through support vector regression.The OED reduces experimental effort by 75–90 % compared to the FFP while only slightly increasing the average uncertainty by 0.30–0.79 %. For the FFP, MoCaVal achieved uncertainties of 0.52–3.9 % for calibrated models and 0.34–0.56 % with ML models. Therefore, within the design space, ML outperforms calibrated models based on the FFP and OED regarding uncertainty. However, since machine learning models were trained on the FFP, additional research is needed to demonstrate their potential for reducing test time and extrapolating results.

Assessments of epidemic spread in aquaculture: comparing different scenarios of infectious bacteria incursion through spatiotemporal hybrid modeling.

The sustainable development of the aquaculture sector is at risk due to the significant challenges posed by many emerging infectious diseases. While disease prevention and control measures are becoming increasingly critical, there is a dearth of studies on the epidemiological aspects of disease transmission in aquatic ecosystems. This study aims to forecast the spread of a bacterial disease between fish farms in two regions, Romsdalsfjord in Norway and Gujwa in South Korea by applying a DTU-DADS-Aqua spatiotemporal hybrid simulation model. The simulation model assessed the pattern of disease transmission between fish farms under different degrees of transmission power based on the distance between farms (ScalingInf), host susceptibility (RelSusceptibility), the origin site of disease, and the capacity of culling fish. The distance between fish farms was found to have significant associations with disease transmission. In most simulation conditions, the disease transmission between different bay management areas (BMAs) was not evident in Romsdalsfjord. In the Guwja region, where there are relatively narrow distances between fish farms, the spread of infectious disease was greatly affected by ScalingInf. The impact of RelSusceptibility on disease transmission patterns is a critical factor to consider in simulation modeling. When RelSusceptibility ranges from 0.5-1, there is little impact on the likelihood of disease transmission. Conversely, lower ranges (0.2 and 0.05) of RelSusceptibility result in a significant decrease in the area affected by the spread of disease. Eradication measures could control the patterns of infectious disease transmission, but the effectiveness of the depopulation strategy can be dramatically changed depending on the geographical environment. In conclusion, through a comparative analysis of the disease transmission and management scenarios, this study demonstrates the potential use of existing simulation models in predicting the spread of infectious diseases under different epidemiological circumstances and quarantine actions.

Технология построения имитационных моделей силовых трансформаторов с произвольной конструкцией активной части

Existing simulation models of power transformers that operate, for example, in the MATLAM Simulink SimPowerSystem environment, are based on an ideal transformer model. It allows using this model as an autonomous link in a complex electrical circuit. The main disadvantage of such models is the difficulty to consider non-standard versions of the magnetic circuit and special schemes for connecting winding elements when modeling. Particularly significant problems arise when modeling special transformers of the certain classes. At the same time, traditionally, when modeling the transient modes of transformers, a different approach is used. It is based on the use of inductance matrices which allows considering all the design features of the active part of the transformer. The disadvantage of this approach is the need to describe an external electrical circuit in addition to the transformer. Therefore, the problem to develop a modern technology for simulation models of transformers with an arbitrary design of the active part is topical. This model meets the requirement for the autonomy of the transformer model from the external circuit model. The authors have used the methods of modeling electrical and magnetic circuits based on the theory of ordinary differential equations, and simulation method using the MatLab Simulink SimPowerSystems package. A technology has been developed for simulation models of transformers with an arbitrary design of the active part based on the use of typical subsystems. An algorithm for matrix of inductances based on the main magnetic field of the transformer with the existing equivalent circuit of the magnetic circuit is given. The authors have presented a diagram of a three-phase transformer model developed using the proposed technology, as well as the results of comparing the current curves in the primary and secondary windings of the transformer when it is turned on at idle and with a resistive load. The results are obtained using the existing and new models. The results of the study can be used to design general industrial and special transformers in design companies and in manufacturing environment. The developed technology can give a special effect when it is used at the R&amp;D stage to study the operating modes of transformers in case the enterprise has no experience to design and manufacture them.