PowerFlow: A Toolbox for Modeling and Simulation of Aircraft Systems

Williams, Matthew; Sridharan, Srikanthan; Banerjee, Subhabrata; Mak, Chris; Pauga, Craig; Krein, P.T.; Alleyne, Andrew G.; Jacobi, Anthony M.; D'Urso, Steven J.

doi:10.4271/2015-01-2417

Cited by 9 publications

(5 citation statements)

References 16 publications

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“…Langland 54) by adding models of turbomachinery useful for air cycle machines that are also part of aircraft thermal management systems. To fill the gaps in multiple modes of power transfer, a paper by Williams et al (56) was one of the first in the open literature to develop a dynamic MATLAB/Simulink toolbox that encompassed mechanical, electrical, thermal, hydraulic, and pneumatic domains for aircraft. This tool was used to develop models of more than 10,000 Simulink blocks for vehicles such as the Boeing 737 as well as the Boeing 787 MEA.…”

Section: Systems Modelingmentioning

confidence: 99%

Control as an Enabler for Electrified Mobility

Alleyne

Aksland

2022

Annu. Rev. Control Robot. Auton. Syst.

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This article outlines the importance of electrified mobility (e-mobility) in modern transport. One key goal of this review is to illustrate the role that control has played, and must continue to play, as e-mobility grows. The coordination of power in multiple modes (mechanical, electrical, and thermal) requires sophisticated controller algorithms. This review advocates for model-based approaches to control since there may not be readily available physical systems from which to gather data and do data-based control. A second goal of the article is to present methods for modeling these powertrain systems that are modular, scalable, flexible, and computationally efficient. A graph-based approach satisfies many of the desired criteria. The third goal is to review control approaches for these classes of systems and detail a hierarchical approach that makes trades across different domains of power. Optimization-based approaches are well suited to achieving the regulation and tracking goals, along with the minimization of costs and the satisfaction of constraints. Multiple examples, within this article and the references therein, support the presentation throughout. This field of e-mobility is rapidly growing, and control engineers are uniquely positioned to have an impact and lead many of the critical developments. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 5 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Section: Systems Modelingmentioning

confidence: 99%

Control as an Enabler for Electrified Mobility

Alleyne

Aksland

2022

Annu. Rev. Control Robot. Auton. Syst.

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“…Model. The electrical graph is validated using nonlinear electrical system components from the PowerFlow toolset which has previously been validated [9]. The architecture pictured in Fig.…”

Section: Validation Of Graphmentioning

confidence: 99%

“…A nonlinear model of the system in Fig. 27 is developed using PowerFlow [9] for the electrical system, ATTMO [8] for the air cycle system, and Thermosys [7] for the single phase thermal-fluid system. Edge and vertex parameters for the graph in Fig.…”

Section: System Graph Validationmentioning

confidence: 99%

“…These toolboxes serve as an excellent tool for developing and analyzing control designs. Several examples of such toolsets include the Thermosys toolbox [7] for modeling air-conditioning and refrigeration systems, the ATTMO toolbox [8] for modeling aircraft vapor cycle systems, and the PowerFlow toolbox [9] for holistic aircraft power system modeling.…”

Section: Introductionmentioning

confidence: 99%

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Dynamical Graph Models of Aircraft Electrical, Thermal, and Turbomachinery Components

Williams

Koeln

Pangborn

et al. 2017

Journal of Dynamic Systems, Measurement, and Control

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The current trend of electrification in modern aircraft has driven a need to design and control onboard power systems that are capable of meeting strict performance requirements while maximizing overall system efficiency. Model-based control provides the opportunity to meet the increased demands on system performance, but the development of a suitable model can be a difficult and time-consuming task. Due to the strong coupling between systems, control-oriented models should capture the underlying physical behavior regardless of energy domain or time-scale. This paper seeks to simplify the process of identifying a suitable control-oriented model by defining a scalable and broadly applicable approach to generating graph-based models of thermal, electrical, and turbomachinery aircraft components and systems. Subsequently, the process of assembling component graphs into a dynamical system graph that integrates multiple energy domains is shown. A sample electrical and thermal management system is used to demonstrate the capability of a graph model at matching the complex dynamics exhibited by nonlinear and empirically based simulation models.

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“…Due to the complexity of the systems and corresponding models, modular, toolbox-based modeling frameworks are often developed. Examples in the fields of building and vehicle energy management include the Thermosys™ [7] toolbox for modeling air-conditioning and refrigeration systems, the ATTMO [8] toolbox for modeling aircraft vapor cycle systems, and the PowerFlow toolbox for holistic aircraft power system modeling [9]. Each of these toolboxes consists of individual component models that can be interconnected to form complete systems.…”

Section: Introductionmentioning

confidence: 99%

Experimental Validation of Graph-Based Modeling for Thermal Fluid Power Flow Systems

Koeln

Williams

Pangborn

et al. 2016

Volume 2: Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Com

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Model-based control design has the ability to meet the strict closed-loop control requirements imposed by the rising performance and efficiency demands on modern engineering systems. While many modeling frameworks develop controloriented models based on the underlying physics of the system, most are energy domain specific and do not facilitate the integration of models across energy domains or dynamic timescales. This paper presents a graph-based modeling framework, derived from the conservation of mass and energy, which captures the structure and interconnections in the system. Subsequently, these models can be used in model-based control frameworks for thermal management. This framework is energy-domain independent and inherently captures the exchange of power among different energy domains. A thermal fluid experimental system demonstrates the formulation of the graph-based models and the ability to capture the hydrodynamic and thermodynamic behaviors of a physical system. 1,

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PowerFlow: A Toolbox for Modeling and Simulation of Aircraft Systems

Cited by 9 publications

References 16 publications

Control as an Enabler for Electrified Mobility

Control as an Enabler for Electrified Mobility

Dynamical Graph Models of Aircraft Electrical, Thermal, and Turbomachinery Components

Experimental Validation of Graph-Based Modeling for Thermal Fluid Power Flow Systems

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