There are strong demands to utilize multi-core computing resources effectively for large-scale and highly detailed multi-agent simulations. We have proposed a framework to assist parameter tuning process of multi-core...
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ISBN:
(纸本)9781479968336
There are strong demands to utilize multi-core computing resources effectively for large-scale and highly detailed multi-agent simulations. We have proposed a framework to assist parameter tuning process of multi-core programming for simulation developers to utilize many parallel cores in their simulation programs efficiently. However, due to its massive computation costs, it is not easy task to seek the sufficient compilation and execution parameters and analyze their performance characteristics for various execution settings. In this paper, we present a preliminary analysis of parameteroptimization based on BLMAB by utilizing our framework. We show how our BLMAB-based approach can effectively be used on the parameter optimization process.
Accurately predicting battery behavior, while using low input data, is highly desirable in embedded simulation architectures like grid or integrated energy system analysis. Currently, the available vanadium redox flow...
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Accurately predicting battery behavior, while using low input data, is highly desirable in embedded simulation architectures like grid or integrated energy system analysis. Currently, the available vanadium redox flow battery (VRFB) models achieve highly accurate predictions of electrochemical behavior or control algorithms, while the optimization of the required input data scope is neglected. In this study, a parametrization tool for a DC grey box simulation model is developed using measurements with a 10 kW/100 kWh VRFB. An objective function is applied to optimize the required input data scope while analyzing simulation accuracy. The model is based on a differential-algebraic system, and an optimizationprocess allows model parameter estimation and verification while reducing the input data scope. Current losses, theoretical storage capacity, open circuit voltage, and ohmic cell resistance are used as fitting parameters. Internal electrochemical phenomena are represented by a self-discharge current while material related losses are represented by a changing ohmic resistance. Upon reducing input data the deviation between the model and measurements shows an insignificant increase of 2% even for a 60% input data reduction. The developed grey box model is easily adaptable to other VRFB and is highly integrable into an existing energy architecture.
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