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A Model Identity Card to Support Simulation Model Development Process in a Collaborative Multidisciplinary Design Environment

机译:可以在多学科协作设计环境中支持仿真模型开发过程的模型身份卡

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摘要

Today, one of the major challenges in full-vehicle model creation is to get domain models from different experts while detecting any potential inconsistency problem before the Integration, Verification, Validation, and Qualification phase. To overcome such challenges, the conceptual design phase has been adapted to the current model development process. For that, the system engineers start to define the most relevant system architecture by respecting quality and time constraints. Next, the simulation model architects design the delivered system architecture in a more formal way with a modeling and simulation point of view to support the integration of domain-level simulation models in a consistent fashion. Finally, the model architects negotiate with different simulation model providers with the aim of specifying vehicle- and domain-level simulation models and their interface connections. To improve knowledge sharing between the mentioned actors, we propose a model identity card (MIC) for classifying simulation model knowledge, including input/output parameters, method, and usage specifications. The fundamental concepts that form the basis of all simulation models are identified and typed for implementation into a computational environment. An industrial case study of the engine–after-treatment model is used to show how MICs and the integrated model design phase might be used in a given scenario. A validation protocol is conducted through a heuristic observation to estimate the rate of model rework and ambiguity reduction.
机译:如今,全车模型创建的主要挑战之一是在集成,验证,验证和鉴定阶段之前,从不同专家那里获取领域模型,同时检测任何潜在的不一致问题。为了克服这些挑战,概念设计阶段已适应当前的模型开发过程。为此,系统工程师开始通过考虑质量和时间限制来定义最相关的系统架构。接下来,仿真模型设计师以更为正式的方式设计建模的系统架构,并从建模和仿真的角度出发,以一致的方式支持域级仿真模型的集成。最后,模型设计师与不同的仿真模型提供者进行协商,以指定车辆和领域级仿真模型及其接口连接。为了改善上述参与者之间的知识共享,我们提出了一种用于对模拟模型知识进行分类的模型身份卡(MIC),包括输入/​​输出参数,方法和使用规范。识别并键入构成所有仿真模型基础的基本概念,以将其实施到计算环境中。发动机后处理模型的工业案例研究用于说明如何在给定场景中使用MIC和集成模型设计阶段。通过启发式观察进行验证协议,以估计模型返工率和歧义减少率。

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