Reduce Simulation Time Research Articles

Topology reduction through machine learning to accelerate dynamic simulation of district heating

District heating networks (DHNs) provide an efficient heat distribution solution in urban areas, accomplished through interconnected and insulated pipes linking local heat sources to local consumers. This efficiency is further enhanced by the capacity of these networks to integrate renewable heat sources and thermal storage systems. However, integration of these systems adds complexity to the physical dynamics of the network, necessitating complex dynamic simulation models. These dynamic physical simulations are computationally expensive, limiting their adoption, particularly in large-scale networks. To address this challenge, we propose a methodology utilizing Artificial Neural Networks (ANNs) to reduce the computational time associated with the DHNs dynamic simulations. Our approach consists in replacing predefined clusters of substations within the DHNs with trained surrogate ANNs models, effectively transforming these clusters into single nodes. This creates a hybrid simulation framework combining the predictions of the ANNs models with the accurate physical simulations of remaining substation nodes and pipes. We evaluate different architectures of Artificial Neural Network on diverse clusters from four synthetic DHNs with realistic heating demands. Results demonstrate that ANNs effectively learn cluster dynamics irrespective of topology or heating demand levels. Through our experiments, we achieved a 27% reduction in simulation time by replacing 39% of consumer nodes while maintaining acceptable accuracy in preserving the generated heat powers by sources.

Using Convolutional Neural Networks in Installation Analysis of Lazy-Wave Flexible Risers

Abstract The design phase of offshore installation projects is supported by numerical simulations. These analyses aim to evaluate the mechanical behavior of the equipment involved, such as vessels and flexible pipes, during that operation. Therefore, a common approach is to take the ocean wave loads modeled as deterministic ones (or regular wave approach), which is a simplification that, on the one hand, allows low computational cost, but, on the other one, lacks the representation of the actual behavior of the wave loads, usually better represented by means of an irregular wave modeling. In the way of searching for an irregular wave analysis procedure to be used in the daily design of lazy-wave riser installation analyses, this work proposes an artificial neural network (ANN)-based approach. The proposed model aims to achieve it by training a convolutional neural network (CNN) fed by generated data from short-length finite element-based numerical simulations. This surrogate model can predict quite well the pipe's top tension and approximately the axial tension in the touchdown zone (TDZ) for different configuration stages during the riser's installation operation. Moreover, the proposed model works for different environmental scenarios, which boosts the computational simulation time reduction in this phase of riser design.

A machine learning assisted multifidelity modelling methodology to predict 3D stresses in the vicinity of design features in composite structures

Multifidelity global–local finite element (FE) analyses are typically used to predict damage initiation hotspots around repetitive design features in large composite structures, such as composite airframes. We propose the use of machine learning (ML) methods to accelerate these analyses. We demonstrate this ML assisted framework for the stress analysis of a hole in plate feature in an aerospace C-spar structure. To enable this framework, we develop the following original features: a computationally efficient sampling scheme; a work-equivalent boundary condition homogenisation scheme; a volume averaged ply-by-ply stress approach; and a sequential long-short term memory neural network reformulated from a time basis to a stacking sequence basis with further bi-directionality customisation. Overall, we show that the developed method results in high-accuracy prediction of 3D stresses, with over two orders of magnitude reduction in modelling and simulation time compared to FE analyses.

Towards transferable metamodels for water distribution systems with edge-based graph neural networks

Data-driven metamodels reproduce the input-output mapping of physics-based models while significantly reducing simulation times. Such techniques are widely used in the design, control, and optimization of water distribution systems. Recent research highlights the potential of metamodels based on Graph Neural Networks as they efficiently leverage graph-structured characteristics of water distribution systems. Furthermore, these metamodels possess inductive biases that facilitate generalization to unseen topologies. Transferable metamodels are particularly advantageous for problems that require an efficient evaluation of many alternative layouts or when training data is scarce. However, the transferability of metamodels based on GNNs remains limited, due to the lack of representation of physical processes that occur on edge level, i.e. pipes. To address this limitation, our work introduces Edge-Based Graph Neural Networks, which extend the set of inductive biases and represent link-level processes in more detail than traditional Graph Neural Networks. Such an architecture is theoretically related to the constraints of mass conservation at the junctions. To verify our approach, we test the suitability of the edge-based network to estimate pipe flowrates and nodal pressures emulating steady-state EPANET simulations. We first compare the effectiveness of the metamodels on several benchmark water distribution systems against Graph Neural Networks. Then, we explore transferability by evaluating the performance on unseen systems. For each configuration, we calculate model performance metrics, such as coefficient of determination and speed-up with respect to the original numerical model. Our results show that the proposed method captures the pipe-level physical processes more accurately than node-based models. When tested on unseen water networks with a similar distribution of demands, our model retains a good generalization performance with a coefficient of determination of up to 0.98 for flowrates and up to 0.95 for predicted heads. Further developments could include simultaneous derivation of pressures and flowrates.

Computational analysis of the failure mechanisms of a laminated composite in double-edge notch compression configuration

This manuscript presents a combined experimental–computational investigation of the compression failure response of laminated composites in double-edge notch compression (DENC) configuration. Analysis of in-situ and post-test DENC experiment results indicates the presence of multiple failure mechanisms, including ply splitting, delamination, and compression kink bands. In order to characterize the onset, growth, and interplay of the critical and subcritical damage mechanisms, a multiscale progressive damage analysis approach is implemented. A nonlocal multiscale damage model explicitly tracks the intraply failure mechanisms that lead to the formation and propagation of kink bands in the specimen. A cohesive zone model is used to track initiation and progression of ply splitting in the specimen. The splitting model was implemented using implicit finite element simulations and deployed with a pre-crack insertion technique to reduce simulation time while preserving prediction accuracy. Model parameters are calibrated based on available calibration data and measurements. The effects of split location and growth characteristics on model predictions are studied. The computational model along with a suite of experiments were employed to study the formation, growth and interactions of damage mechanisms in the composite specimens subjected to compression loading. The onset of matrix damage is not clear from the experimental images but the model predicts early onset around the onset time of splits which greatly reduce the stress concentration at the notches. Analysis of acoustic emission energy experimental data indicate that observable compression failure mechanisms are not causing nonlinearity consistently exhibited in the experiment responses. Simulation results demonstrate the capability of the multiscale modeling approach to predict critical kink bands and sub-critical matrix cracking and splitting in the DENC specimens while reducing computational cost compared to a direct numerical simulation of fibers and matrix.

Computed chest radiography for total body irradiation: image quality and clinical feasibility

Objective. In myeloablative total body irradiation (TBI), lung shielding blocks are used to reduce the dose to the lungs and hence decrease the risk of radiation pneumonitis. Some centers are still using mega-Volt (MV) imaging with dedicated silver halide-based films during simulation and treatment for lung delineation and position verification. However, the availability of these films has recently become an issue. This study examines the clinical performance of a computed radiography (CR) solution in comparison to radiographic films and potential improvement of image quality by filtering and post-processing. Approach. We compared BaFBrI-based CR plates to radiographic films. First, images of an aluminum block were analyzed to assess filter impact on scatter reduction. Secondly, a dedicated image quality phantom was used to assess signal linearity, signal-to-noise ratio (SNR), contrast and spatial resolution. Ultimately, a clinical performance study involving two impartial observers was conducted on an anthropomorphic chest phantom, employing visual grading analysis (VGA). Various filter materials and positions as well as post-processing were examined, and the workflow between CR and film was compared. Main results. CR images exhibited high SNR and linearity but demonstrated lower spatial and contrast resolution when compared to film. However, filtering improved contrast resolution and SNR, while positioning filters inside the cassette additionally enhanced sharpness. Image processing improved VGA scores, while additional filtering also resulted in higher spine visibility scores. CR shortened TBI simulation by over 10 minutes for one patient, alongside a dose reduction by order of 0.1 Gy. Significance. This study highlights potential advantages of shifting from conventional radiographic film to CR for TBI. Overall, CR with the incorporation of processing and filtering proves to be suitable for TBI chest imaging. When compared to radiographic film, CR offers advantages such as reduced simulation time and dose delivery, re-usability of image plates and digital workflow integration.

A comprehensive exploration of approximate DNN models with a novel floating-point simulation framework

This paper introduces TorchAxf11https://github.com/knuscent/TorchAxf., a framework for fast simulation of diverse approximate deep neural network (DNN) models, including spiking neural networks (SNNs). The proposed framework utilizes various approximate adders and multipliers, supports industrial standard reduced precision floating-point formats, such as bfloat16, and accommodates user-customized precision representations. Leveraging GPU acceleration on the PyTorch framework, TorchAxf accelerates approximate DNN training and inference. In addition, it allows seamless integration of arbitrary approximate arithmetic algorithms with C/C++ behavioral models to emulate approximate DNN hardware accelerators.We utilize the proposed TorchAxf framework to assess twelve popular DNN models under approximate multiply-and-accumulate (MAC) operations. Through comprehensive experiments, we determine the suitable degree of floating-point arithmetic approximation for these DNN models without significant accuracy loss and offer the optimal reduced precision formats for each DNN model. Additionally, we demonstrate that approximate-aware re-training can rectify errors and enhance pre-trained DNN models under reduced precision formats. Furthermore, TorchAxf, operating on GPU, remarkably reduces simulation time for complex DNN models using approximate arithmetic by up to 131.38× compared to the baseline optimized CPU implementation. Finally, we compare the proposed framework with state-of-the-art frameworks to highlight its superiority.

Acceleration of computations in modelling of processes in complex objects and systems

The development of methods of parallelization of computing processes, which involve the decomposition of the computational domain, is an urgent task in the modeling of complex objects and systems. Complex objects and systems can contain a large number of elements and interactions. Decomposition allows you to break down a system into simpler subsystems, which simplifies the analysis and management of complexity. By dividing the calculation area of the part, it is possible to perform parallel calculations, which increases the efficiency of calculations and reduces simulation time. Domain decomposition makes it easy to scale the model to work with larger or more detailed systems. With the right choice of decomposition methods, the accuracy of the simulation can be improved, since different parts of the system may have different levels of detail and require appropriate methods of additional analysis. Decomposition allows the simulation to be distributed between different participants or devices, which is relevant for distributed systems or collaborative work on a project. In this work, mathematical models are built, which consist in the construction of iterative procedures for "stitching" several areas into a single whole. The models provide for different complexity of calculation domains, which makes it possible to perform different decomposition approaches, in particular, both overlapping and non-overlapping domain decomposition. The obtained mathematical models of subject domain decomposition can be applied to objects and systems that have different geometric complexity. Domain decomposition models that do not use overlap contain different iterative methods of "stitching" on a common boundary depending on the types of boundary conditions (a condition of the first kind is a Dirichlet condition, or a condition of the second year is a Neumann condition), and domain decomposition models with an overlap of two or more areas consist of the minimization problem for constructing the iterative condition of "stitching" areas. It should be noted that the obtained models will work effectively on all applied tasks that describe the dynamic behavior of objects and their systems, but the high degree of efficiency of one model may be lower than the corresponding the degree of effectiveness of another model, since each task is individual. Keywords: mathematical modelling, decomposition of the computational domain, parallelization, optimization, complex objects and systems.

Thermally-stable solar energy absorber structure with machine learning optimization

Major problems that the world is currently confronting include global warming and the energy crisis, and the optimal absorption of solar spectrum radiation is a pivotal stage for a green future for the effective usage of solar thermal energy. The available solar absorbers are temperature sensitive and do not provide perfect absorption for a wide operation range. The major objectives of the proposed paper are to overcome these limitations and present an ultrawideband thermally stable solar absorber. The developed absorber utilizes a high melting point material comprising a Ti reflector element, a Si3N4 dielectric layer, and a pi-shaped top TiN metal layer. Within the spectral range spanning from 200 to 2500 nm, a notable average absorption rate of 97.69 % is attained. This structure has also demonstrated exceptionally under the AM1.5 conditions with a spectral absorption efficiency of 96 %. This work presents a novel optimization approach for solar absorbers, employing K-nearest neighbor (KNN) regression models to enhance design optimization by simulation time reduction with an R2 score of 0.999. The novelty resides in the absorber surface shape, utilization of cost efficient, sustainable, and thermally stable materials, as well as the KNN regression model for optimization of proposed solar absorber structure. Due to the angle and polarization insensitive characteristics, this structure can be potentially utilized for solar energy harvesting and shielding.

Optimal sizing of standalone hybrid renewable energy system based on reliability indicator: A case study

The economical electrification of rural settlements extensively utilizes renewable energy sources. In recent years, the deployment of solar photovoltaic, wind turbine, and biomass gasifier-based power generation technologies for electricity accessibility in remote regions has received greater prominence. This paper presents an optimal component design of a hybrid solar PV-wind and battery system along with a biomass gasifier to provide continuous electricity for rural communities of Uttarakhand, India. In this study, a recently developed population-based giza pyramids construction (GPC) methodology is applied to determine the optimal component sizing. The findings of the GPC method are contrasted with the particle swarm optimization (PSO) methodology to assess the effectiveness of the proposed method. The annualized system cost and levelized cost of energy of the hybrid renewable energy system have been minimized subject to technical reliability indicators such as loss of power supply probability and renewable fraction.Moreover, a systematic exploration of the system has been conducted to assess its optimal feasibility across different values of power loss probability ranging from (0% to 4%). The simulation indicates that the proposed method adeptly identifies globally optimal values, leading to a $177 decrease in annual cost and 8% reduction in average simulation time compared to PSO. The research reveals that the balanced point (relative attainment), where system reliability and cost-effectiveness intersect optimally, is achieved at a 3% power loss level, resulting in the most significant cost reduction of $199 compared to other probability levels.

Advancing blast fragmentation simulation of RC slabs: A graph neural network approach

Accurate prediction of blast-induced fragmentation in reinforced concrete (RC) structures is pivotal for structural debris hazard assessment in an explosion event. This assessment is essential for designing effective measures for protection of people and structure in the vicinity. While conventional computational techniques are proficient, they are computationally very demanding and may not be readily adaptable in practical applications. This study proposes a novel approach, the Fragment Graph Network (FGN), a specialized variant of graph neural networks (GNNs), to simulate complex interactions between macro-level particles of RC structures subjected to close-in blast loads. The RC structure is discretised into particles, with the model predicting structural responses and fragmentation based on states of these particles. Utilizing an autoregressive prediction mechanism, our model can accurately simulate spatiotemporal responses of RC slabs against blast loads. The efficacy of the model was tested on a dataset consisting of 149 simulated cases, encompassing around 180,000 states each, covering a wide range of blasting scenarios and structural design parameters including TNT charge weight, standoff distance, slab dimension, concrete strength, and reinforcement ratio and layout. Notably, the proposed model delivered accurate predictions, achieving relative errors under 10% and R2 values exceeding 0.93 for both standard and extrapolated test scenarios as compared to the SPH simulations. Remarkably, our model dramatically outpaced traditional numerical approaches, reducing simulation times from hours to minutes on CPUs and seconds on GPUs. This pioneering effort paves the way for efficient and accurate simulations of RC blast fragmentations, offering a rapid alternative to standard numerical approaches.

Advancing simulation efficiency for MEMS electrostatic actuators

This paper presents an overview of the research progress on Multiphysics simulation of MEMS electrostatic actuators. MEMS electrostatic actuators have shown significant development potential in applications such as ADWSS and VOA. However, the increasing size and complexity of these devices have posed challenges to traditional Multiphysics simulation methods, leading to lengthy computational times that hinder the overall device development process. In order to enhance simulation efficiency and meet the demands of device development, the use of simplified single physics field models have been explored. The results have demonstrated that the simplified single physics filed simulation model yields comparable results to traditional Multiphysics simulations with an error tolerance of within 10%, while reducing simulation time by 80-90%.

Finite element boundary element numerical simulation study on acoustic vibration coupling problems

Excessive structural resonance response in the acoustic vibration coupling environment can cause adverse consequences such as structural damage, local instability, and component failure. Accurately predicting the vibration response of instruments under external sound fields helps to avoid structural performance degradation or damage caused by vibration, and ensures the reliability and safety of the system, which is of great significance for the effective load of instrument equipment. This paper constructs a finite element boundary element equation for the acoustic vibration coupling problem, and adopts a series of optimization measures for it. In response to the low computational efficiency of finite element boundary element equations and their inability to meet the computational requirements of complex models, this paper introduces the acoustic vibration reciprocity theorem and the fast directional boundary element method to compensate for the aforementioned shortcomings. In addition, the study used the weighted residual method to construct a data exchange algorithm to ensure energy conservation during the transfer process. The research focuses on typical structures of spacecraft and conducts simulation analysis and application research on acoustic vibration coupling problems. The simulation showcases that the introduction of the reciprocity theorem markedly reduces the calculation time and memory occupied by the equation, with a required calculation time of 1.23 h and a data file size of approximately 299.56 MB generated by the calculation; The overall error of the data exchange process is below 10−4. Scanning frequency analysis is conducted on the surface and local openings of the model structure, and it is found that the displacement of the selected excitation point is in good agreement with the analytical solution. This study effectively reduces simulation time and memory requirements through the proposed strategy, ensures energy conservation data exchange algorithms, improves the reliability and safety of structural design, and has important practical value for evaluating the structural performance of key equipment such as spacecraft.

Determining appropriate level of detail of 3D aggregate model for accurate and efficient mesoscopic simulation of asphalt concrete

The levels of detail (LODs) of aggregate models significantly impact both the accuracy and efficiency of the mesoscale numerical simulation of asphalt concrete. This study proposed a quantitative evaluation framework to find an appropriate LOD that allows accurate simulation of asphalt concrete with relatively low model complexity. First, the coarse aggregates in asphalt concrete were reconstructed from computed tomography (CT) images, and their LOD was defined as level-0. Each reconstructed aggregate was then simplified using a surface simplification method that obtains aggregate models with four descending LODs (levels-1, 2, 3 and 4), consisting of 200, 100, 50 and 20 triangular facets respectively. The simulation accuracy was evaluated according to several aggregate geometric indexes and mesoscopic finite element (FE) compressive simulation results of asphalt concrete. The results indicate that compared to the level-1 model, the level-2 model can achieve a similar simulation accuracy with a 50% reduction in simulation time.

Novel method for temperature prediction in rotary kiln process through machine learning and CFD

Blast furnace high-proportion pellet smelting stands as a pivotal pathway towards achieving low-carbon and environmentally friendly ironmaking. However, challenges in pellet ore production, particularly through the rotary kiln method, have surfaced due to the scarcity of iron ore resources, leading to issues like “ringing” that hamper production efficiency and energy conservation. Effectively predicting and optimally controlling the temperature field within rotary kilns emerges as a crucial strategy to address this pressing challenge. In a pioneering effort, this paper introduces a data-driven machine learning methodology designed to expedite the prediction of temperature field distributions within rotary kilns. Through a detailed analysis of field data from the factory and the insights of experts, the paper has discussed on the key factors influencing the temperature field dynamics in rotary kilns. Leveraging Computational Fluid Dynamics (CFD), a numerical simulation model was established to replicate rotary kiln oxy-coal combustion scenarios. A comprehensive dataset was generated using high-throughput computing technology, encompassing a variety of conditions such as coal injection rates, return air temperature, return airflow volume, fixed carbon content of pulverized coal, and volatile fraction of pulverized coal. This dataset included a total of 625 distinct cases. Subsequently, employing machine learning techniques including random forest (RF) and genetic algorithm-optimized Random forest (GA-RF), predictive models were developed for rotary kiln temperature field dynamics. A rigorous comparative analysis was conducted to explore the impact of different machine learning dataset sizes on the predictive accuracy of the models. The results of this analysis demonstrate that, enhanced by the genetic algorithm of RF, it surpasses the performance of the standard RF approach. This enhancement resulted in a significant improvement in prediction accuracy, as evidenced by the reduction of the Root Mean Square Error (RMSE) from 25.0879 to 17.5330 and the Mean Absolute Error (MAE) from an initial value of 6.5600 to 4.7661. Furthermore, the novel soft-measurement method introduced in this paper significantly reduces simulation time by several orders of magnitude. It has diminished the response time from the traditional industrial methods, which took two to three weeks, to approximately 10 s, as compared to conventional CFD approaches. Remarkably, this approach successfully preserves a high level of accuracy. The implications of these research findings are far-reaching,providing novel insights into the acceleration of CFD simulation calculations and the design of soft measurement sensors customized for black-box reactors in industrial processes.

A hybrid MLP-CNN model based on positional encoding for daytime radiative cooler

Radiative cooling is a groundbreaking technology for sustainable temperature regulation to combat global warming and the urban heat island effect. Traditional optimization algorithms for cooling system structures are often brute-force and even limited to local optimal solutions. Here, we propose a joint simulation method based on a multilayer perceptron (MLP) and a convolutional neural network (CNN) with position encoding (PE), namely the hybrid MLP-CNN model with PE. This method can significantly reduce simulation time, avoid getting trapped in local optima, and accurately predict the optical response and radiative cooling power of the structure. Applying PE enables the MLP model to learn the relationship between structural parameters and optical response more quickly and effectively, reducing loss during training and more minor root mean square error (RMSE) and mean relative error (MRE) during testing. In addition, the residual network can reduce the problem of overfitting in the network. Compared to using only an MLP, the hybrid MLP-CNN with PE model can effectively reduce loss and achieve more accurate prediction outputs. The hybrid model is much faster than traditional electromagnetic simulations, taking only 1% of the time, and the model dramatically reduces the time needed to predict the optical response of structures and effectively decreases the cost of designing optical devices. Utilizing this hybrid model not only avoids the potential drawbacks of being limited to suboptimal solutions and significantly reduces the simulation time, but also effectively predicts the optical response of the device. This approach can demonstrate remarkable adaptability in optimizing geometric parameters of various optical apparatuses, including polarization converters, beam splitters, metalens, and other two-dimensional nanostructures.

Efficient AI-Physics hybrid model with productive capabilities to reduce the time of history matching and scenario assessment; a case study: Minagish oil field

This paper proposes a novel approach that combines physics-based numerical simulation with deep-learning neural networks to create an AI-Physics hybrid model for reservoir simulation. Our primary objective is to reduce the time of history match and achieve the best match between model and observation data using our hybrid model. We trained our model using historical data and created a reliable forecasting model that predicts field behavior and proposes new scenarios to improve production in the upcoming years. To test our model, we combined AI physics history training with blind test prediction calculations of the remaining oil map, and the AI physics model created a forecast scenario definition based on this map. Our proposed simulation method can reduce the time of history matching and scenario evaluation by 90-95%. We created three improved forecast scenarios based on predefined scenarios that can produce millions of standard barrels more oil over three years than the original development scenario. The results demonstrate the potential of our AI-Physics hybrid model in revolutionizing reservoir simulation in the oil and gas industry. Our approach can significantly reduce simulation time, improve forecasting accuracy, and optimize production strategies, leading to significant economic benefits.

Transfer Matrix Model for Emission Profile Optimization of Radial Gratings

AbstractRadial Bragg gratings are commonly used to enhance light extraction from quantum emitters, but lack a well‐suited, fast simulation method for optimization beyond periodic designs. To overcome this limitation, an algorithm based on the transfer matrix model (TMM) to calculate the free‐space emission of such gratings is proposed and demonstrated. Using finite difference time domain (FDTD) simulations, free‐space emission, and transfer matrices of single grating components are characterized. The TMM then combines any number of components to receive the total emission. Randomized benchmarks verify that results from this method agree within 98% with FDTD while reducing simulation time by one to two orders of magnitude. The speed advantage of this approach is shown by maximizing emission of a fifteen‐trench circular grating into a Gaussian mode. It is expected that this novel algorithm will facilitate the optimization of radial gratings, enabling quantum light sources with unprecedented collection efficiencies.

A Deep Learning-Based CAE Approach for Simulating 3D Vehicle Wheels Under Real-World Conditions

The implementation of deep learning (DL) in computer-aided engineering (CAE) can significantly improve the accuracy and efficiency of simulating 3D vehicle wheels under real-world conditions. While traditional CAE methods can be time-consuming and computationally expensive, DL can reduce simulation time and development cycles across all industries. This work explores the role of DL and AI in virtual manufacturing and CAE and investigates how they can be used to improve the accuracy and efficiency of simulations for 3D vehicle wheels. Deep learning models can learn the complex relationships between different wheel design parameters, such as tire load distribution, stress distribution, and fatigue life. Once trained, these models can be embedded into CAE software, allowing for faster and more accurate simulations of wheel performance. This interdisciplinary study uses various deep learning techniques, including convolutional neural networks (CNNs), generative adversarial networks (GANs), and recurrent neural networks (RNNs), to create a more efficient and accurate relationship between CAD modeling and CAE simulation. The research aims to leverage the potential of deep learning models to automate 3D CAD design, accurately predict CAE results, and provide in-depth explanations and verifications. The benefits of this research are expected to extend to the automotive industry's pursuit of more robust and resilient wheel designs. By streamlining the product development process from conceptual design to engineering performance evaluation, this study has the potential to revolutionize the automotive industry's product development cycle.

Deduction and Application of the Average Switch Model in Power Electronic Devices for Simulation Time Reduction

Context: Time is a crucial issue in the simulation of power electronics (PE) devices, even more when these elements are integrated into microgrids.Method: This paper deals with the deduction of the average switch model for PE devices with the purpose of reducing simulation times. For doing this, the average model is only applied over the power switches of PE devices, not being applied over the complete topology as traditionally done. The proposed average model switch permits eliminating the ripple of voltage and currents but keeping the transient of the signals. The average model switch is derived for Boost and Buck converter switches and then generalized to power inverter switches. The proposed approach is validated using OpenModelica software.Results: A system featuring a battery, a DC/DC converter, and an inverter connected to the power grid was simulated. A comparison was performed between a simulation that considers the power switches and a simulation that uses the proposed average model switch, the time simulation was reduced up to 99.788 %, which validates the proposed approach.Conclusions: The proposed average switch model significantly reduces simulation times. This method offers a promising way to streamline power electronics device simulations, particularly in the context of microgrids and other applications where time efficiency is critical.