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Technology Commercialization and Partnerships

Advanced Computational Tools for Combustion Analysis and Engine Design

Argonne has developed a suite of four advanced computational tools for addressing complex challenges related to combustion analysis and engine design

ML-GA: Machine-Learning Genetic Algorithm (ANL-SF-18-098)
Argonne’s ML-GA software provides a unique capability for rapid design optimization by combining machine learning (ML) and genetic algorithm (GA) techniques. It employs ML (either one or multiple ML algorithms can be incorporated) to predict the quality (merit) of a design from the input parameters. Then, a stochastic global optimization genetic algorithm (GA) is used with the machine learning model as the objective function to optimize the input parameters based on the merit function. ML-GA is scalable to high-performance computing platforms such as supercomputers, enabling optimization to be performed in significantly short time frames (of the order of a few days).

As a proof-of-concept, the potential of the ML-GA approach coupled with computational fluid dynamics (CFD) was demonstrated for optimization of a heavy-duty internal combustion engine operating under medium load conditions. For more information on this application of ML-GA: A Machine Learning-Genetic Algorithm (ML-GA) Approach for Rapid Optimization Using High-Performance Computing

Technical Contact:
Pinaki Pal, Energy Systems

LESI: Lagrangian-Eulerian Spark Ignition Model (ANL-SF-18-030)
Argonne has developed an approach for spark-ignition modeling of complex engine conditions, for use within industry CFD solver packages, such as Convergent Science’s CONVERGE framework. Argonne’s LESI model allows for enhanced accuracy in spark-ignition modelling of internal combustion engines and extends current capabilities to more challenging real-world conditions.  This is an important upgrade for the automotive industry, as spark-ignition engine technologies move toward unconventional boosted and dilute operation that impact a wider range of performance factors, such as flame propagation, cycle-to-cycle variation (CCV), and spark-plug durability. Additionally, compression ignition strategies are also increasingly reliant on ignition systems to control combustion behavior. Predictive models coupled with high-performance computing (HPC) can evaluate advanced combustion concepts and accelerate the development of high-efficiency engines.

Argonne’s LESI model leverages previous findings that have expanded the use and improved the accuracy of Eulerian-type energy deposition models. The Eulerian energy deposition is coupled at any computational time-step with a Lagrangian-type evolution of the spark channel. Typical features such as spark channel elongation, stretch, and attachment to the electrodes are properly described to deliver realistic energy deposition along the channel during the entire ignition process. This is a decisive factor to accurately describe ignition processes in a highly-dilute and highly-turbulent environment.

Journal article: Development of a Hybrid Lagrangian-Eulerian Model to Describe Spark-Ignition Processes at Engine-Like Turbulent Flow Conditions

Technical Contact:
Riccardo Scarcelli, Principal Mechanical Engineer, Energy Systems

PPM4CCV: Parallel Perturbation Model for Cycle-to-Cycle Variability (ANL-SF-17-030)
PPM4CCV is a pre-processing approach to modelling cyclic variability in spark ignition (SI) engines that can be coupled with any major engine CFD platform (e.g., CONVERGE CFD, AVL-Fire or STAR-CD). The parallel perturbation method overcomes several challenges associated with predicting cyclic variability, resulting in up to a 10x speed-up in computation times compared to conventional approaches. By implementing PPM4CCV, engine developers can not only free up computational resources, but also accelerate the engine design process.

Cycle-to-cycle variability (CCV) is known to be detrimental to SI engine operation resulting in partial burn and knock, and an overall reduction in the reliability of the engine. Modelling CCV in SI engines is challenging because computationally intensive high-fidelity methods are required; and CCV is experienced over long timescales requiring simulations to be performed over hundreds of consecutive cycles. The PPM4CCV approach is to perform multiple parallel simulations, each of which encompasses multiple cycles, by perturbing simulation parameters such as the initial and boundary conditions. More information: Parallel Methodology to Capture Cyclic Variability in Motored Engines  

Technical Contact:
Muhsin Ameen, Mechanical Engineer, Energy Systems

TESF: Tabulated Equivalent SDR Flamelet Model (ANL-SF-16-159)
Argonne’s TESF software consists of an implementation of a novel tabulated combustion model for non-premixed flames in CFD solvers. This novel technique is used to implement an unsteady flamelet tabulation without using progress variables for non-premixed flames. It also has the capability to include history effects which is unique within tabulated flamelet models. The flamelet table generation code can be run in parallel to generate tables with large chemistry mechanisms in relatively short wall clock times. This framework can be coupled with any CFD solver with Reynolds-averaged Navier–Stokes (RANS) and Large Eddy Simulation (LES) turbulence models. This framework enables CFD solvers to run large chemistry mechanisms with a large number of grids at relatively low computational costs. Currently it has been coupled with the CONVERGE CFD code and validated against available experimental data. This model can be used to simulate non-premixed combustion in a variety of applications like reciprocating engines, gas turbines and industrial burners operating over a wide range of fuels. More information: An Equivalent Dissipation Rate Model for Capturing History Effects in Non-Premixed Flames; Implementation of Detailed Chemistry Mechanisms in Engine Simulations

Technical Contact:
Prithwish Kundu, Mechanical Engineer, Energy Systems

 Business & Licensing Contact (for all software listed above):
Greg Halder, Business Development Executive, Technology Commercialization and Partnerships