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AVL FIRE™ Release 2019 R1 & AVL FIRE™ M Release 2019 R1

With this new version, the focus was placed again on extending the software’s scope of applicability and on adding capabilities that allow users to perform their tasks faster, better and with less effort. This is especially important in an era characterized by a rapid change of employed technologies.

To satisfy the increased demand for R&D on electrified powertrains, an electro-chemical model supporting the development of Li-Ion batteries has been integrated into AVL FIRE™ M 2019R1. The new release also allows simulating the thermal behavior of batteries using the RC Model, which is available in an identical manner in AVL CRUISE™ M.

To meet customer demands for cleaner and more efficient IC Engines, the capabilities for simulating fuel injection, combustion, emission formation and exhaust gas aftertreatment have been further improved and extended.

Detailed information about all new capabilities, extensions, enhancements and changes in AVL FIRE™ and AVL FIRE™ M 2019R1 are listed in the release notes. Here are the highlights:


Developing Electrified Powertrains


The electrification of powertrains is a clear move towards significantly lower pollutant emissions. To enable engineers to develop and integrate the underlaying technologies, systems and individual components, AVL FIRE™ and AVL FIRE™ M offer a broad spectrum of solutions.

Battery Electro-chemical Model

New in FIRE™ M v2019 is the possibility to perform an analysis and optimization of Li-Ion batteries deploying a detailed, non iso-thermal, electro-chemical model. The model calculates the spatial and temporal distributions of solid and liquid lithium concentrations, electronic and ionic potentials, reaction current density and temperature in the current collectors, electrodes and separator. Anisotropic electrical conduction, heat conduction and lithium diffusion in the porous electrodes are fully accounted for. The electrochemical reaction is calculated with the Butler-Volmer equation. Most of the parameters used in the electrochemical model are material parameters and are therefore part of the property database (PDB) of the AVL Simulation Desktop (SDT).  SDT is the platform in which AVL FIRE™ M is integrated.

The electro-chemical battery model in FIRE™ M supports the development of Li_Ion cells. The figure shows State of charge (left) and temperature (right) during the simulation of nail penetration.

The model allows to judge and improve the performance of Li-Ion batteries, typically on a cell or module level, based on design and material parameters. It also enables the identification of hot spots potentially causing degradation and performing short circuit analysis.

Equivalent Circuit Model / RC Model

For the thermal analysis of batteries, FIRE™ M 2019R1 offers, besides the already known semi-empirical Shepard model, a newly implemented equivalent circuit model (RC-Model). The input for the equivalent circuit model are resistance and capacitance of RC elements depending on temperature and state of charge. Among simulation engineers it is generally agreed that these quantities are easier to determine as those required as input for the Shepard model. In the same way as for the semi-empirical model, the battery parameterization wizard, which is directly connected to FIRE™ M, can be used to generate the RC parameters based on several current jump experiments. Comparing the performance of the RC-Model and the Shepard model, the former is faster.

Requiring only commonly available input data, the equivalent circuit model supports a broader use of the FIRE™ battery thermal analysis capabilities.

The newly integrated equivalent circuit model enables an easier setup and faster computing of battery thermal load

Battery Venting

With 2019R1 a solution is provided to run battery venting simulations, imitating the sudden release of hot gases through a safety vent and the heat transfer to neighboring cells.

With increasing heat release in a cell the material of the vent is melting and eventually an opening is formed that allows the hot gases to exit the cell and to propagate through the battery housing. Doing so, heat is transferred from the gas to other battery cells, which, potentially, could damage these cells as well.

The solution for battery venting helps to assess the safety of a battery. 

Making batteries safer: Hot gas exiting a battery cell through a safety vent


Thermal Load Analysis

AVL FIRE™ M offers a comprehensive set of capabilities supporting the analysis of the thermal load of components and systems. This includes the automated multi-domain mesh generation, advanced heat transfer modelling considering effects of nucleate boiling, variable material property data handling, accounting for contact resistances when propagating heat, computing v. Mises stress and deformation due to thermal load.

Wall Condensation

New in FIRE™ M 2019 R1 is a wall condensation and evaporation model. It can be used to simulate the condensation of moist air at cold walls, e.g. in heat exchangers.  It describes the diffusion driven process in near-wall regions and works in both directions, for condensation and evaporation. The model considers the thickening and reduction of the flow boundary layer in order to compute the heat transfer between fluid and structure domains as well as fluid domains and environment.

The new wall condensation model in FIRE™ M improves the calculation of heat transfer between fluid and structure and supports a more accurate thermal load analysis.
The picture shows a wall condensation model applied to a heat exchanger. The model improves the accuracy of the heat transfer calculation.

Component Finder

Already with previous versions of FIRE™ M a so-called HBC Component Finder was provided. The tool enables fast and reliable identification of all components of head block compound (HBC) geometries helping to make the preparation of models for thermal load analysis easier and faster and to avoid user errors. Within seconds the tool identifies components and even parts of components and assigns Selections to these elements. This results in a tremendous time saving, as the interactive approach would typically last several hours. Components which are geometrically identical are grouped, which later again simplifies and accelerates the simulation setup.

A major enhancement of the tool available with 2019R1 is the automated re-construction of the cooling water jacket. The latest version of the Component Finder closes inlets and outlets of the cooling jacket automatically, thus enabling also the safe identification of this domain.

The enhanced HBC Component finder of 2019R1 further reduces the time required for model pre-processing.
Saving a lot of time: Further enhanced component finder to prepare head block compound analysis


IC Engine-based Powertrains

Water Injection

To simulate gasoline engines with additional water injection FIRE™ v2018.1 offers a set of updates in the Spray and Combustion Modules.  These ensure fast and consistent handling of species transport, flame speed calculation and computation of the equivalence ratio within the framework of the ECFM-3Z Model.

With 2019R1 FIRE™ offers additional features enabling a fast, dedicated and detailed post-processing and analysis of gasoline / water injection engines.
v2019R1 offers additional analysis options for gasoline / water injection simulations. The figure shows water (blue dots) injected into a GDI Engine during intake stroke.

Injection Nozzle

A key application of AVL FIRE™ is the simulation of the injection nozzle internal, cavitating and eroding multiphase flow. In combination with the 1D high pressure hydraulic system solution AVL BOOST™ / HYDSIM, such a simulation also accounts for the impact of the three-dimensionally moving injector needle on the flow, phase change, phase distribution, areas of erosion and discharge rate.

To enable the use of AVL FIRE™ for nozzle flow simulations in co-simulation with third-party high pressure hydraulics modelling solutions, 2019R1 offers exporting a FIRE™ Multiphase flow models as Functional Mock-up Unit (FMU). Such an FMU can be used by any software that supports the Functional Mock-up Interface (FMI) standard. As supporting this standard became quite common during the past decade, this new functionality significantly broadens the spectrum of tools that can be interfaced with FIRE™ for nozzle flow simulations or other multiphase flow problems.

The FMU Export of FIRE™ Multiphase models supports collaboration between development teams by offering to use and re-use 3D Component models in other simulation environments. This is expected to improve the accuracy and the performance of a problem analysis.
FIRE™ FMU including a multiphase nozzle flow model with input and output channels for simple setup of co-simulations with 3rd party tools

Exhaust Aftertreatment

The focus of our recent work was on accelerating the calculation of wall film and deposit formation in SCR Systems. Such simulations require to represent physics and chemistry of several minutes of real time. Therefore, to accomplish such tasks significant compute resources are needed.

Already with the releases of FIRE™ v2017 and v2018 a significant reduction in compute time was achieved by offering both, improvements in the simulation tool as well as in the simulation method.

With FIRE™ 2019 R1 another significant speed-up of AdBlue wall-film and deposit formation simulations could be achieved. Tests have shown that 2019R1 performs up to 5 times better for this particular application, due to the implementation of an improved “database method”.
The newly implemented “database-method” accelerated the computation of AdBlue wall film and deposit formation simulations significantly.



Proximity-based Grid Refinement

FIRE™ M 2019R1 offers automatic proximity-based mesh refinement. The software identifies narrow regions and refines the mesh down to a predefined minimum cell size. Thus, it helps to avoid overlooking geometry details when setting up the mesh generation process and to ensure a good grid quality for fast convergence and high solution accuracy. To avoid refinements in regions of lower interest, the activation of the feature can be confined to specific regions by linking it to one or multiple Selections.

The feature helps to ensure the right mesh density in all parts of the computational domain while avoiding excessive grid refinements.

Proximity-based refinement is an enabler for fast and safe meshing of geometries with features of very different sizes. It helps to avoid excessively large meshes and to achieve great mesh quality simultaneously.


Mesh Embedding

Some applications require the use of particularly structured grids, e.g. the simulation of reaction chemistry in catalysts or soot loading and regeneration in particle filters. For such cases users often generate “mixed meshes” deploying both block-structured or extruded meshes for the catalysts and automated grid generation for all other parts of the model. In that case the two pre-processing steps are required and then the two meshes need to be connected.

With FIRE™ M 2019 R1 the pre-processor offers a feature called Mesh Embedding. It allows to set up grids for the above described use cases in a single step and without the need to define any interface for connecting the different mesh parts. Instead the user only marks the parts that require a dedicated mesh topology on the CAD Model by means of Selections. Providing the common input for the automatic grid generation process, such as minimum and maximum cell size, the meshing process can be started and completed in a single step. The result will be a mesh consisting of parts that feature automatically generated polyhedral elements and parts that meet specific requirements about cell orientation. All parts are connected with each other in a conform manner.

The feature is a great time saver and contributes to high simulation accuracy. 
Generating a “mixed mesh”, consisting out of completely different mesh types with no effort is now available with FIRE™ 2019R1


Ease of Use

In-Situ Post-processing

With 2019R1, FIRE™ M offers so-called In-Situ post-processing for the analysis of single phase flow problems. The feature allows defining views, cut types, quantities to be plotted, their scaling and output frequency already during the setup of a simulation task. During run time, the FIRE™ M Solver uses this specification to directly generate the desired images. Thus, the user can see and analyze results according to the simulation progress and without having to start and operate the post-processor. This also means that In-Situ post-processing avoids excessive memory allocation when viewing results of large models. Even no result file is required for In-Situ post-processing and the output frequencies for In-Situ and result file writing are independent from each other.

In-Situ postprocessing thus provides quasi-online result viewing with little effort.
FIRE™ M 2019R1 allows defining the plots you want to see already when setting up a simulation. This reduces the effort for an interactive operation of IMPRESS™ M.