Co-modelling Strategy for Development of Airpath Metamodel on Multi-physics Simulation Platform

This paper introduces a comprehensive and systematic Design of Experiments based methodology deployed in conjunction with a multi-physics engine air-path and combustion co-simulation, leading to the development of a global transient simulation capability for engine out NOx emissions. The proposed multi-physics engine simulation framework couples a real-time one-dimensional air flow model with a Probability Density Function based Stochastic Reactor Model that accounts for detailed in-cylinder combustion chemistry to predict combustion emissions. The integration challenge stemming from the different computation complexities and time scales required to ensure adequate fidelity levels across multi-physics simulations was addressed through a comprehensive Design of Experiments methodology to develop a reduction of the slower Stochastic Reactor Model simulation to enable a transient simulation focussed on NOx emissions. The Design of Experiments methodology, based on Optimal Latin Hypercube design experiments, was deployed on the multi-physics engine co-simulation platform and systematically validated against both steady state and transient light-duty Diesel engine test data. The surrogate selection process included the evaluation of a range of metamodels, with Kriging metamodels selected based on both the statistical performance criteria and consideration of physical phenomena trends. The transient validation was carried out on a simulated New European Drive Cycle against the experimental data available, showing good capability to capture transient NOx emission behaviour in terms of trends and values. The significance of the results is that it proves the transient and drive cycle capability of the multi-physics simulation platform, suggesting a promising potential applicability for early powertrain development work focussed on drive cycle emissions.

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Progress on Developing a Multi-physics Simulation Platform: Rigorous Advanced Plasma Integration Testbed (RAPIT)

2018 Plasmadynamics and Lasers Conference ◽

10.2514/6.2018-2944 ◽

2018 ◽

Cited By ~ 1

Author(s):

Yun-Min Lee ◽

Meng-Hua Hu ◽

Cheng-Chin Su ◽

Kuan-Lin Chen ◽

Meng-Fan Tseng ◽

...

Keyword(s):

Simulation Platform ◽

Physics Simulation

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A novel medical image data-based multi-physics simulation platform for computational life sciences

Interface Focus ◽

10.1098/rsfs.2012.0058 ◽

2013 ◽

Vol 3 (2) ◽

pp. 20120058 ◽

Cited By ~ 10

Author(s):

Esra Neufeld ◽

Dominik Szczerba ◽

Nicolas Chavannes ◽

Niels Kuster

Keyword(s):

Treatment Planning ◽

Medical Image ◽

Life Sciences ◽

Image Data ◽

Human Anatomy ◽

Simulation Platform ◽

Anatomical Models ◽

Wide Range ◽

Physics Simulation ◽

Medical Image Data

Simulating and modelling complex biological systems in computational life sciences requires specialized software tools that can perform medical image data-based modelling, jointly visualize the data and computational results, and handle large, complex, realistic and often noisy anatomical models. The required novel solvers must provide the power to model the physics, biology and physiology of living tissue within the full complexity of the human anatomy (e.g. neuronal activity, perfusion and ultrasound propagation). A multi-physics simulation platform satisfying these requirements has been developed for applications including device development and optimization, safety assessment, basic research, and treatment planning. This simulation platform consists of detailed, parametrized anatomical models, a segmentation and meshing tool, a wide range of solvers and optimizers, a framework for the rapid development of specialized and parallelized finite element method solvers, a visualization toolkit-based visualization engine, a P ython scripting interface for customized applications, a coupling framework, and more. Core components are cross-platform compatible and use open formats. Several examples of applications are presented: hyperthermia cancer treatment planning, tumour growth modelling, evaluating the magneto-haemodynamic effect as a biomarker and physics-based morphing of anatomical models.

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