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Methodology for Automated Tuning of Simulation Models for Correlation with Experimental Data

机译：用于与实验数据相关的仿真模型自动调整的方法

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In this paper a practical methodology for automated tuning of simulation models is introduced, which is widely and successfully adapted in IAV. For this, stochastic optimization algorithms (like Genetic Algorithms or Particle Swarm Optimization), and appropriate algorithms for optimization tasks with very long computation time (e.g. Adaptive Surrogate-Model Optimization or Adaptive Hybrid Strategies) are used in combination with commercial and internal simulation tools. Often it is necessary to evaluate several contradictory objectives at the same time which leads to multi-criterion optimization. Effective post processing methods (mathematical decision aids) are used to select the best compromises for the problem. As a practical example, this automated tuning methodology is applied to an engine performance simulation model developed in GT-Power. Procedure of multi-criterion optimization for co-relation of output parameters like rate of heat release, burn duration, 90% mass fraction burned etc. is explained in detail. It is observed that, time required for simulation model tuning is reduced by up to 75% w.r.t. conventional methods of model tuning. A good co-relation w.r.t. experimental data is achieved even for cases with lots of parameters and multiple operation points.
机译：本文提出了仿真模型的自动调谐实用方法的引入，它被广泛和成功地适应在IAV。为此，随机优化算法（如遗传算法或粒子群算法），以及用于优化任务合适的算法有很长的计算时间（例如自适应代孕，模型优化和自适应混合战略）结合使用商业和内部仿真工具。通常，需要同时导致多目标优化评估几种矛盾的目标。有效的后处理方法（数学决策辅助）来选择问题的最佳妥协。作为一个实际的例子，该自动调谐方法被应用到在GT-电源开发的发动机性能的仿真模型。输出参数，如热释放速率的共关系的多目标优化的过程中，燃烧持续时间，90％质量分数燃烧等进行详细说明。据观察，，所需的仿真模型调谐时间是由高达75％w.r.t.减少模型调谐的常规方法。一个良好的合作关系w.r.t.实验数据甚至有很多的参数和多个工作点的情况下实现的。

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