Optimal Architecture Selection for an Aircraft Environmental Control System
John B. Finn

Citation
John B. Finn. "Optimal Architecture Selection for an Aircraft Environmental Control System". Master's thesis, University of California Berkeley, May, 2015.

Abstract
Cyber Physical Systems (CPS) are characterized by a tight coupling between the physical and the computational worlds. As system complexity and heterogeneity increase, it is becoming more difficult to perform design space exploration at the system level and analyses between the system architecture and control algorithms. Often, designers are expected to solve combinatorial problems over a large discrete variable space that is coupled to a continuous space, where expensive, high-fidelity simulations must be run to achieve the desired accuracy. In this thesis, I build upon a design methodology, based on the principles of Platform Based Design, to address the challenges associated with next generation CPS. The CPS design methodology is a series of three steps including: Architecture Selection, Control Synthesis and Verification/Optimization. I extend Architecture Selection to support a mixed discrete and continuous design space. The proposed approach is an iterative process, where a discrete architecture Selection engine is placed in a loop with a continuous Sizing engine. First, the Selection engine proposes a candidate architecture to the Sizing engine. Then, the Sizing engine attempts to optimize continuous parameters subject to formalized design requirements, which are monitored using simulation. If the Sizing engine cannot find a feasible solution, the Selection engine is queried for another candidate architecture and the process repeats. I illustrate the methodology on an industrial case study, namely an aircraft Environmental Control System. Finally, I show how balance equations and conservation laws can be used to prune the discrete search space and reduce the number of simulations. In my experiments, I obtain more than an order of magnitude reduction in design runtime.

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Citation formats

• HTML

  John B. Finn. <a
  href="http://www.icyphy.org/pubs/61.html"
  ><i>Optimal Architecture Selection for an Aircraft
  Environmental Control System</i></a>, Master's
  thesis,  University of California Berkeley, May, 2015.

• Plain text

  John B. Finn. "Optimal Architecture Selection for an
  Aircraft Environmental Control System". Master's
  thesis,  University of California Berkeley, May, 2015.

• BibTeX

  @mastersthesis{Finn15_OptimalArchitectureSelectionForAircraftEnvironmental,
      author = {John B. Finn},
      title = {Optimal Architecture Selection for an Aircraft
                Environmental Control System},
      school = {University of California Berkeley},
      month = {May},
      year = {2015},
      abstract = {Cyber Physical Systems (CPS) are characterized by
                a tight coupling between the physical and the
                computational worlds. As system complexity and
                heterogeneity increase, it is becoming more
                difficult to perform design space exploration at
                the system level and analyses between the system
                architecture and control algorithms. Often,
                designers are expected to solve combinatorial
                problems over a large discrete variable space that
                is coupled to a continuous space, where expensive,
                high-fidelity simulations must be run to achieve
                the desired accuracy. In this thesis, I build upon
                a design methodology, based on the principles of
                Platform Based Design, to address the challenges
                associated with next generation CPS. The CPS
                design methodology is a series of three steps
                including: Architecture Selection, Control
                Synthesis and Verification/Optimization. I extend
                Architecture Selection to support a mixed discrete
                and continuous design space. The proposed approach
                is an iterative process, where a discrete
                architecture Selection engine is placed in a loop
                with a continuous Sizing engine. First, the
                Selection engine proposes a candidate architecture
                to the Sizing engine. Then, the Sizing engine
                attempts to optimize continuous parameters subject
                to formalized design requirements, which are
                monitored using simulation. If the Sizing engine
                cannot find a feasible solution, the Selection
                engine is queried for another candidate
                architecture and the process repeats. I illustrate
                the methodology on an industrial case study,
                namely an aircraft Environmental Control System.
                Finally, I show how balance equations and
                conservation laws can be used to prune the
                discrete search space and reduce the number of
                simulations. In my experiments, I obtain more than
                an order of magnitude reduction in design runtime.},
      URL = {http://icyphy.org/pubs/61.html}
  }
  

Posted by John B. Finn, IV on 6 Sep 2015.
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