CIERA: Cavitation-Induced Erosion Risk Assessment Model (ANL-SF-19-118)
Argonne has developed an approach for modeling cavitation-induced erosion for use with any multiphase computational fluid dynamics (CFD) code (e.g. CONVERGE, ANSYS Fluent, etc.). Argonne’s CIERA model provides a framework for linking multiphase flow predictions with the response of the solid material. This link is represented via an energy balance at the fluid-solid interface, which considers the cumulative energy absorbed by the solid material from repeated hydrodynamic impacts. In contrast to existing methods, Argonne’s CIERA model provides more reliable prediction of erosion severity by considering both impact load and duration.
Application of CIERA to simulations of multiphase flow systems can guide design studies in improving system durability and reducing maintenance costs. To date, CIERA’s erosion prediction capabilities have been demonstrated in simulations of pressurized fuel through aluminum channels and a heavy-duty fuel injector, and validated against available experimental data. For more information on the CIERA model: Evaluation of a new cavitation erosion metric based on fluid-solid energy transfer in channel flow simulations; Linking cavitation collapse energy with the erosion incubation period
Gina Magnotti, Research Scientist, Energy Systems Division
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.
Riccardo Scarcelli, Research Scientist, Energy Systems
ML-GA: Machine-Learning Genetic Algorithm (ANL-SF-18-098; ANL-SF-19-073)
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
Pinaki Pal, Research Scientist, 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
Muhsin Ameen, Research Scientist, 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
Prithwish Kundu, Research Scientist, Energy Systems
Business & Licensing Contact (for all software listed above):
Eric Tyo, Business Development Executive, Technology Commercialization and Partnerships
Related Open Source Software
TF-MoE: Tabulated Flamelet – Mixture of Experts (ANL-SF-19-174)
Argonne has developed a deep learning driven approach for modeling turbulent combustion that provides a framework for incorporating high-dimensional datasets in computational fluid dynamics (CFD) simulations in a tractable and efficient manner, with 2-5 times speed-up over traditional methods. This open source software enhances predictive modeling via machine learning. Argonne’s TF-MoE software employs a mixture of experts (MoE) approach to bifurcate high-dimensional tabulated flamelet (TF) data into simpler manifolds in a physically intuitive manner. It employs a divide-and-conquer competitive approach, where different zones in the manifold are assigned to various neural networks for inference. It consists of two classes of neural networks, namely, a gating network which is a neural network classifier, and a number of experts which are neural network regressors. The software bifurcates the manifold by having different neural networks compete for each input signal. The gating network rewards the best predictors with stronger signals during subsequent training episodes and feeds poor-performing networks with weaker signals. The gating network and experts are trained using a standard backpropagation approach.
As a proof-of-concept, the accuracy and efficiency of TF-MoE was demonstrated in an a priori study of high-dimensional tabulation for modeling of non-premixed combustion using the flamelet approach. For more information on TF-MoE: Efficient bifurcation and parameterization of multi-dimensional combustion manifolds using deep mixture of experts: an a priori study
TF-MoE is available through GitHub: https://github.com/owoyeleope/TFM-MoE
Ope Owoyele, Postdoctoral Researcher, Energy Systems Division
Pinaki Pal, Research Scientist, Energy Systems Division