Process Automation of Exhaust System Optimization using
Acusolve, HyperStudy
Ganesh Krishna Babar
Nagarajan G
PLN Prasad
Manager
Mahindra & Mahindra Ltd.
MRV Chennai-603204, India
[email protected]
Manager
Mahindra & Mahindra Ltd.
MRV Chennai-603204, India
NAGARAJAN.G@mahindra.
com
Deputy General Manager
Mahindra & Mahindra Ltd
MRV Chennai-603204, India
[email protected]
Abbreviations:
Computational Fluid Dynamics (CFD), Flow Uniformity Index (FUI), HyperStudy (HST)
Keywords:
AcuSolve, Top Cone, Optimization, CATCON
Abstract
In the race to optimize powertrain performances of tomorrow, engine developers are
focusing their efforts in multiple areas. These areas include new combustion processes, intake and exhaust systems. The
optimization of exhaust system for optimum back pressure helps for better fuel economy & less emissions.
Objective of exhaust system Optimisation is to minimise the back pressure & improve flow uniformity index at CATCON inlet.
This procedure allows to optimise the parameters in a single simulation. Use of Hyperstudy along with Hypermesh & AcuSolve
is used to automate the process.
Introduction:
HyperStudy is the optimiser which can be linked with HyperMesh & Acusolve for the automation of the
optimization process. Initially the geometry is imported in the HyperMesh. Complete clean-up is done
& boundaries are defined like, inlet, outlet, interface, wall etc. The shell mesh generated & exported to
AcuConsole. Here all the volume mesh parameters, materials, boundary conditions are defined. The
geometry is deleted & the problem set is saved as template.
Read the template file in HyperStudy. Add & define the models, design variables, register the solver
script for Acusolve. To ensure the setup perform the base run & then define responses. After this the
number of runs need to be defined. It will solve for the defined number of models. Use the simple
python script to extract the required parameters like FUI, pressure etc. Postprocess the results to
understand the flow characteristics across the domain.
HyperStudy Optimazation process:
Here the workflow of the whole optimization process.
Fig1: Basic Automation Process
Correlation study for the baseline model:
The study is done with base model to validate the AcuSolve results. The results of the study is
tabulated below. It shows the solution time for both the models not varying much. The convergence was
fast with AcuSolve. The base study helps to understand the flow characteristics across the domain. The
velocity profiles across the top cone sections help to locate the recirculation zones. After understanding
the complete flow profile the modifications are planned. Grid independent study was carried out in
AcuSolve to arrive the mesh size of 0.4 to 0.6 mm. For optimization the model is coarsened to mesh size
of 1.5 to 2.0 mm to save the computational time.
FVM based Tool:
FUI: 0.861
AcuSolve (Fine Mesh):
FUI: 0.858
Fig 2: CFD models used for the analysis & corresponding FUI
AcuSolve (Coarse Mesh):
FUI: 0.806
Table-1: Correlation study statistics
Tool\ITR
Acusolve
Cell
Count
3863476
Node
Count
718806
No of
CPU
2
FVM based tool
795592
150972
2
Convergence
FUI
200
Time(minut
es)
~540
0.858
1166
~430
0.861
Generating the morphed Shapes:
After postprocessing the baseline model study the modifications are planned in the
identified regions. The modifications are done by using HyperMorph tool panel of the HyperMesh.
HyperMorph is used to morph the shapes to desired extent. HyperMorph is capable of morphing the
complicated shapes & range of options to get the required shape.
Fig 3: Few of the Morphed models created in HyperMesh
AcuConsole Template Definition:
AcuSolve is the CFD solver used in the optimization process. It is required to define the volume mesh
parameters, material & boundary conditions in the template file. HST will make sure that these
parameters are set once the new morphed shape is imported in the AcuSolve. Here it gives the
freedom to set the prism boundary layers, mesh biasing parameters for smooth transition.
Fig 4: Problem setup in AcuSolve
Interfacing creation with HST:
Once the shapes are created & template is defined in the AcuSolve it needs to interface with HST.
Here the solver script is registered & responses are defined. To ensure the setup nominal run is given.
Then it is run for the number of given iterations.
Fig 5: Interfacing AcuSolve with HST
Results & Discussions:
Table-2: FUI at CATCON inlet for different iterations
ITR
1
2
3
4
5
6
7
8
9
10
mod-1
mod-2
mod-3
mod-4
mod-5
mod-6
mod-7
mod-8
mod-9
mod-10
0.803
0.810
0.806
0.804
0.811
0.820
0.821
0.827
0.833
0.853
0.803
0.812
0.805
0.806
0.813
0.816
0.822
0.828
0.835
0.859
0.806
0.812
0.806
0.806
0.816
0.814
0.824
0.830
0.839
0.863
0.807
0.813
0.808
0.807
0.822
0.813
0.825
0.831
0.841
0.868
0.810
0.814
0.810
0.812
0.825
0.813
0.825
0.833
0.843
0.866
0.812
0.814
0.812
0.812
0.824
0.811
0.828
0.834
0.846
0.865
0.813
0.814
0.814
0.814
0.824
0.813
0.830
0.833
0.848
0.861
0.815
0.814
0.817
0.814
0.826
0.816
0.831
0.833
0.849
0.858
0.816
0.815
0.817
0.811
0.826
0.818
0.833
0.831
0.856
0.856
0.818
0.815
0.817
0.812
0.824
0.821
0.833
0.833
0.863
0.854
FUI of the optimised model after refinement:
After getting the optimised model it is refined with a mesh size of 0.4 to 0.6 mm. This optimised model
is again run with refined mesh to get the actual FUI.
Baseline Model
Fig 6: Velocity Contours across CATCON inlet
Optimised Model
FUI: 0.91
Fig 7: Optimised model after refinement
Benefits Summary
Designing CATCON to have FUI in the range of .85 to 0.9 is really challenging.
Top cone geometry is very much sensitive to FUI. This methodology is reducing huge preprocessing
time & HSTs interface with HyperMesh & AcuSolve makes it simple to try large number of modifications
in the same model. It gives strong understanding of the flow behaviour & helps to propose the
modifications. The convergence of the process is fast & it can further speed up with coarse mesh. Once
reached the final design then the model can be refined to get the actual FUI.
Challenges
Selecting the proper morphing technique from the vast range of tools available with
HyperMorph need very good understanding of the HyperMesh. Also as per our optimization scope
deciding the preprocessing parameters to define the template file decides the time required for the
complete optimization cycle.
Future Plans
 The methodology will be deployed on optimization of similar powertrain systems like
induction, cooling & lubricating systems
To model multiple porous regions in a single model with different flow resistances
Conclusions
Interfacing HyperMesh & AcuSolve with HST makes the optimization process simple, speedy & time
effective. The computational resource requirement is less.