5.3 REALISATION OF A HIGH THROUGHPUT COMPUTING SYSTEM
5.4.2 The Complete HTC System
The HTC system implemented during this thesis includes the components described in the previous Section and a python script - „condorManager.py‟ that manages the communication between HTCondor running jobs and DEMO. As described earlier, the mapping algorithm runs the CAD and meshing programs and then runs the CFD solver at various outlet pressures to obtain the compressor speed line. The mapping algorithm workflow is illustrated in Figure 5.24.
The number of candidates per generation was approximated to 180 (23 X 8 = 184). A batch size of 45 candidates was chosen to run on the 8 desktop machines (32 processors) so that most of the processors are utilised all the time. The batch version of DEMO was then configured to optimise the CAD models in batches of 45. Hence 45 directories were created in the central manager machine in the condor pool for each candidate CAD model to be solved. The full system works as follows:
1. DEMO generates 45 CAD model input parameters.
2. „condorManager.py‟ splits the 45 input parameters into 45 separate directories. 3. „condorManager.py‟ instructs HTCondor to dispatch the jobs to the cluster.
4. „condorManager.py‟ delivers to DEMO the completed batch of results and DEMO produces another batch of 45 candidates and the process continues.
This process is illustrated again Figure 5.25 where N is the batch size and 23 represents the number of parameters input for the CAD algorithm.
Figure 5.25 - Full Illustration of the HTC System
„condorManager.py‟ also calls the following sub programs – „checkJobsLog.py‟, „mergeOutputFiles.py‟
and „Accumulator‟. „condorManager.py‟ calls „checkJobsLog.py‟ at defined intervals set by the user (e.g. every 5 seconds) to check the job logs in each of 45 folders for completion status and notifies
„condorManager.py‟ when all jobs are finished. When all jobs are finished, „condorManager.py‟ calls „mergeOutputFiles.py‟ to merge the „sim_out.dat‟ file (i.e. calculated objectives) of each CAD model in
the different folders into a single file for DEMO to read in. „condorManager.py‟ then calls „Accumulator‟ which collates relevant input and output parameters for each valid CAD model in each set up folder (45
information required to recreate evaluated compressor geometry are stored. Three log files of each valid CAD model are maintained as described below:
1. „paremetersLog.dat‟ containing logs of CAD input parameters 2. „objectivesLog.dat‟ containing logs of calculated objectives
3. speedLineLog.dat‟ containing logs of each compressor operating speed line
„condorManager.py‟ then sends an e-mail with statistics of valid designs along with an attachment of log
files to relevant project team members. A sample e-mail notification is show in Figure 5.26 and a picture of the 8 desktop machines in the condor pool is shown in Figure 5.27.
CONCLUSION
This Chapter detailed the bespoke parametric CAD algorithm developed during this thesis to facilitate multi-objective optimisation of radial compressors. It also details the implementation of an HTCondor enabled HTC system and work flow of sub-programs within the HTC system. In the next Chapter, multi- objective optimisation of an existing HP compressor of a two-stage turbocharger using the HTC system will be presented.
CHAPTER 6
AERODYNAMIC OPTIMISATION OF A
RADIAL COMPRESSOR IN THE HP STAGE OF
A TWO-STAGE TURBOCHARGER
This Chapter presents the multi-objective aerodynamic optimisation of a radial compressor in the high pressure (HP) stage of a two-stage turbocharger facilitated by the HTC system realised in Chapter 5. A new optimised compressor design with significant improvement in efficiency (up to 1.6 points) and 20% increase in map width across three speed lines was achieved, compared to a reference compressor that was designed incrementally. Compressor map width is the difference between the largest and smallest mass flow on a compressor speed line. A speed line is a collection of operating points, each containing information about mass flow, pressure ratio and efficiency.
The contributions of this Chapter are as follows:
A demonstration of multi-objective optimisation of a radial compressor with up to 8 conflicting objectives, including map width enhancement
The discovery of new impeller blade design topology with large blade angles at the exit (or back sweep angle). The large blade angle impeller designs showed higher efficiency, but lower pressure ratio. However, since the HP stage compressor pressure ratio is merely around 2:1, the low pressure ratio of large back sweep impeller is not a disadvantage
Parameter Coarse Medium Fine 1 Span wise mesh cells on blade 28 34 42 2 Span wise mesh cells on tip clearance 4 4 4 3 Upstream meridional mesh cells 26 34 38 4 Inducer meridional mesh cells 26 34 50 5 Passage meridional mesh cells 30 45 65 6 Diffuser meridional mesh cells 45 60 65
7 Pitch wise cells 14 18 27
8 Pitch wise cells on leading edges 4 4 5 9 Mesh growth rate from boundaries 1.3 1.3 1.3
Total number of cells 136672 272393 603992 Table 6.1 - Mesh details for mesh sensitivity study
Figure 6.1b – Blade-to-blade mesh description
The CFD settings for the mesh convergence study case are shown in Table 6.2. The geometry is a high pressure (HP) compressor with inlet stagnation pressure of 350000 Pa and inlet temperature of 60oC
(333K). The pressure ratio of the HP compressor is 2:1, resulting in an outlet static pressure ratio of 700000 at the design speed of 25000 rpm. CFL number is set to a TBLOCK [6] recommended value of 0.4. CFL number sets the solver time steps i.e. how quickly it iterates to find a solution. Smaller values result in a more stable iteration, but longer run time. Smoothing factor is used to scale the CFL number within the solver. A recommended value is chosen for this study. The plot of speed line for the three mesh levels is shown in Figure 6.2. The Figure shows that the target mass flow for all three mesh levels was within 1% of each other at the design pressure ratio of 2:1 as illustrated in Figure 6.2. Overall, the result shows there is no significant difference between the medium and fine mesh in terms of mass flow and efficiency prediction. As a result, the medium mesh is chosen for use in the optimisation calculations.
Figure 6.2 – Convergence study of sample compressor geometry
Mesh topology and quality for each mesh density level is depicted in Figures 6.3 to 6.5. Figures 6.3a, 6.4a and 6.5a show the aspect ratio of cells in the main passage is about 1, except near walls or leading edges where it goes up to 20. Aspect ratio is a measure of how „square‟ a 2D mesh cells is. A perfect square has an aspect ratio of 1. Aspect ratios close to 1 are ideal. Aspect ratios up to 40 may be permissible for structured meshes particularly near boundary layers where fluid properties changes rapidly in the direction normal to the surface and less so in the tangential direction. Figures 6.3b, 6.4b and 6.5b depict mesh skewness for each mesh level to be between 0 and 0.2. A perfect square or cube has a skewness of zero, while a rhombus has a skewness greater than 0 but less than 1. An almost flat rhombus will have skewness approximately 1. Skeweness value close to 1 is very bad for CFD. Figures 6.3 to 6.5 show that the mesh settings used for mesh convergence study produced meshes with very good aspect ratios and skewness values. Figures 6.3c, 6.4c and 6.5c depict mesh topology at the hub for the coarse, medium and fine mesh respectively.
Figure 6.5a – Meridional view of fine mesh showing mesh aspect ratio
Figure 6.5c – Hub view of fine mesh topology
6.2
VALIDATION OF CFD SOLVER
TBLOCK and experimental results for sample compressor geometry are compared for validation purposes. Mesh details for the TBLOCK analysis is shown in Table 6.3. The medium mesh from the mesh convergence study was used for the validation study. TBLOCK boundary conditions and settings are outlined in Table 6.4. Normalised values of speed and capacity are presented in Figure 6.6 which compares experimental and TBLOCK CFD compressor maps. It shows good agreement in terms of width and height of map for the range of speed lines compared as indicated. Also, the peak efficiency of 84% is
Parameter Value Shroud tip clearance 0.5% Span wise mesh cells on blade 34 Span wise mesh cells on tip clearance 4 Upstream meridional mesh cells 34 Inducer meridional mesh cells 34 Passage meridional mesh cells 45 Diffuser meridional mesh cells 60 Pitch wise cells 18 Pitch wise cells on leading edges 4 Mesh growth rate from boundaries 1.3
Table 6.3 – Mesh settings for TBLOCK
Parameter Value
Design Operating speed (rpm) 25000 Inlet stagnation pressure (Pa) 350000 Inlet stagnation temperature (Kelvin) 333 Outlet static Pressure at design point (Pa) 700000
CFL number 0.4
Smoothing factor 0.03
(a) Experiment (b) TBLOCK CFD Figure 6.6 – Comparison of experiment and TBLOCK compressor maps