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Research Papers

Performance Study of GasTurbnLab, an Agent-Based Multi-Physics Problem Solving Environment for the Gas Turbine Engine Simulation

[+] Author and Article Information
Panagiota E. Tsompanopoulou

Department of Computer and Telecommunication Engineering, University of Thessaly, 38221 Volos, Greeceyota@inf.uth.gr

J. Comput. Inf. Sci. Eng 8(3), 031008 (Aug 21, 2008) (8 pages) doi:10.1115/1.2966384 History: Received January 07, 2007; Revised March 19, 2008; Published August 21, 2008

Multiphysics applications are real world problems with a large number of different shape components that obey different physical laws and manufacturing constraints and interact with each other through geometric and physical interfaces. They demand accurate and efficient solutions and a modern type of computational modeling, which designs the whole physical system with as much detail as possible. The simulation of gas turbine engine is such a multiphysics application and is realized with GasTurbnLab, an agent-based Multiphysics Problem Solving Environment (MPSE). Its performance and evaluation study is presented in this paper. For this, a short description of the software components and hardware infrastructure is given. The performance and the scalability of the parallelism are depicted, and the communication overhead between agents is studied with respect to the number of agents and their location in the “computational grid.” The execution time is recorded, and its analysis verifies the complexity of the solvers in use and the performance of the available hardware. Three different clusters of INTEL Pentium processors were used for experimentation to study how the communication time was affected by processor’s homogeneity/heterogeneity and the different connections between the processors. The study of the numerical experiments shows that the domain decomposition and interface relaxation methodology, along with the usage of agent platforms, does not increase the complexity of the simulation problem, and the communication cost is too low, compared with the computations, to reflect on the total simulation time. Therefore, GasTurbnLab is an efficient example of a complex physical phenomena simulation.

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Copyright © 2008 by American Society of Mechanical Engineers
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Figures

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Figure 7

Three block diagrams of engine configurations, with added complexity in boundary connections and number of engine parts

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Figure 8

Communication time (log) versus the size of the data moved, where all agents are in different agencies (a) and only SimpleSCA and SimpleKIVA (diamond line) are in the same agency (b). All data are from experiments in shamu cluster nodes

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Figure 1

View of a gas turbine showing some of its detail, some of its operational characteristics and the engineering methodologies involved in its design, simulation and construction

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Figure 2

Communication between agents

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Figure 3

(a) Blade row flow field—ALE3D. (b) Combustor analysis—KIVA

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Figure 4

Block diagram of the stator-rotor simulation using two AleAgents (left) and the stator-combustor simulation using one AleAgent and one KivaAgent (right)

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Figure 5

In (a), the mass flow rate moves toward a steady state value after 500 KIVA time steps (5000 corresponding ALE3D time steps) in the stator-combustor simulation. In (b), plot for speed (top) and z velocity (bottom), with the stator upstream and the combustor downstream. The simulation involves an AleAgent, a KivaAgent and the BasicMA.

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Figure 6

Plot of a row of stators upstream and a row of rotors downstream after 10000 iterations. The mediation along the interface boundary involves force, flux and velocity data, with three mediator exchanges per time step.

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Figure 9

Execution time (log scale) vs the size of the problem on a shamu machine

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