Hybrid Dynamic Modelling of Engine Emissions on Multi-Physics Simulation Platform

2021 ◽  
Vol 14 (2) ◽  
Author(s):  
Gaurav Pant ◽  
Felician Campean ◽  
Aleksandrs Korsunovs ◽  
Daniel Neagu ◽  
Oscar Garcia-Afonso
Keyword(s):  
Dynamic Modelling ◽  
Simulation Platform ◽  
Engine Emissions ◽  
Physics Simulation

Validation of a multi-physics simulation platform for engine emissions modelling

2021 ◽  
pp. 146808742110643
Author(s):  
Aleksandrs Korsunovs ◽  
Oscar Garcia-Afonso ◽  
Felician Campean ◽  
Gaurav Pant ◽  
Efe Tunc
Keyword(s):  
Design Of Experiments ◽  
Model Simulation ◽  
Transient Simulation ◽  
Performance Criteria ◽  
Nox Emissions ◽  
Simulation Platform ◽  
Reactor Model ◽  
Drive Cycle ◽  
Physics Engine ◽  
Physics Simulation

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.

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

2018 ◽  
Author(s):  
Yun-Min Lee ◽  
Meng-Hua Hu ◽  
Cheng-Chin Su ◽  
Kuan-Lin Chen ◽  
Meng-Fan Tseng ◽  
...  
Keyword(s):  
Simulation Platform ◽  
Physics Simulation

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

2013 ◽  
Vol 3 (2) ◽  
pp. 20120058 ◽  
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.

scholarly journals The use of flow diagnostics to rank model ensembles

2021 ◽  
Author(s):  
Francesca Watson ◽  
Stein Krogstad ◽  
Knut-Andreas Lie
Keyword(s):  
Residence Time ◽  
Oil Recovery ◽  
Dynamic Modelling ◽  
Flow Characteristics ◽  
Diagnostic Techniques ◽  
Flow Diagnostics ◽  
Physics Simulation ◽  
Residence Time Distributions ◽  
Model Ensembles ◽  
Steady State Simulation

AbstractEnsembles of geomodels provide an opportunity to investigate a range of parameters and possible operational outcomes for a reservoir. Full-featured dynamic modelling of all ensemble members is often computationally unfeasible, however some form of modelling, allowing us to discriminate between ensemble members based on their flow characteristics, is required. Flow diagnostics (based on a single-phase, steady-state simulation) can provide tools for analysing flow patterns in reservoir models but can be calculated in a much shorter time than a full-physics simulation. Heterogeneity measures derived from flow diagnostics can be used as proxies for oil recovery. More advanced flow diagnostic techniques can also be used to estimate recovery. With these tools we can rank ensemble members and choose a subset of models, representing a range of possible outcomes, which can then be simulated further. We demonstrate two types of flow diagnostics. The first are based on volume-averaged travel times, calculated on a cell by cell basis from a given flow field. The second use residence time distributions, which take longer to calculate but are more accurate and allow for direct estimation of recovery volumes. Additionally we have developed new metrics which work better for situations where we have a non-uniform initial saturation, e.g., a reservoir with an oil cap. Three different ensembles are analysed: Egg, Norne, and Brugge. Very good correlation, in terms of model ranking and recovery estimates, is found between flow diagnostics and full simulations for all three ensembles using both the cell-averaged and residence time based diagnostics.

Design Support for 3D System Integration by Multi Physics Simulation

2008 ◽  
Author(s):  
Peter Schneider ◽  
Sven Reitz ◽  
Joern Stolle ◽  
Roland Martin ◽  
Andreas Wilde ◽  
...  
Keyword(s):  
System Integration ◽  
Design Support ◽  
Physics Simulation
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