Dynamic Simulation of Aircraft Environmental Control System Based on Flowmaster

Tu, Yubin; Lin, Guiping

doi:10.2514/1.c031433

Cited by 34 publications

(17 citation statements)

References 3 publications

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“…Tool-centric: Specific tools are used to control and orchestrate co-simulation process. In our previous researches [21,20], we developed a platform to manage co-simulation between AMESim [40], Flowmaster [34], Saber [8], Matlab/Simulink [26] and LMS Motion [27]. Co-simulation between hardware and software: Hardware and software communicate with each other in order to implement real-time simulation [19,14].…”

Section: Definitions and Scopementioning

confidence: 99%

A Tool Integration Language to Formalize Co-simulation Tool-Chains for Cyber-Physical System (CPS)

Törngren

Chen

et al. 2018

Software Engineering and Formal Methods

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Section: Definitions and Scopementioning

confidence: 99%

A Tool Integration Language to Formalize Co-simulation Tool-Chains for Cyber-Physical System (CPS)

Törngren

Chen

et al. 2018

Software Engineering and Formal Methods

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“…Furthermore, airborne testing is often infeasible due to the certification processes associated with installation of testing equipment and the time commitment required of the aircraft. For these reasons, modeling of ACMs and environmental control systems is well documented [20][21][22][23][24], but often not validated with flight data.…”

Section: Empirical Validation Of Graphmentioning

confidence: 99%

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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“…However, they mostly entail ad hoc procedures, characterized by a poor automation level and largely neglect the interactions between architecture selection and the control algorithm. Some of these works seek to optimize the heat exchanger size for fuel efficiency, but pay little attention to control design [10]; other approaches only focus on the design of the control protocol, by either evaluating the tradeoffs between a finite number of candidate ECS architectures with respect to temperature control and efficiency [11], or by exploring control strategies based on fuzzy logic [12]. A correct-by-construction supervisory control protocol synthesized from linear temporal logic (LTL) requirements, along with a state-estimation algorithm has also been recently reported [13]; however, the architecture sizing problem is not taken into account.…”

Section: Related Workmentioning

confidence: 99%

A mixed discrete-continuous optimization scheme for Cyber-Physical System architecture exploration

Finn

Nuzzo

Sangiovanni‐Vincentelli

2015

2015 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

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Abstract-We propose a methodology for architecture exploration for Cyber-Physical Systems (CPS) based on an iterative, optimization-based approach, where a discrete architecture selection engine is placed in a loop with a continuous sizing engine. The discrete optimization routine proposes a candidate architecture to the sizing engine. The sizing routine optimizes over the continuous parameters using simulation to evaluate the physical models and to monitor the requirements. To decrease the number of simulations, we show how balance equations and conservation laws can be leveraged to prune the discrete space, thus achieving significant reduction in the overall runtime. We demonstrate the effectiveness of our methodology on an industrial case study, namely an aircraft environmental control system, showing more than one order of magnitude reduction in optimization time.

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Dynamic Simulation of Aircraft Environmental Control System Based on Flowmaster

Cited by 34 publications

References 3 publications

A Tool Integration Language to Formalize Co-simulation Tool-Chains for Cyber-Physical System (CPS)

A Tool Integration Language to Formalize Co-simulation Tool-Chains for Cyber-Physical System (CPS)

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

A mixed discrete-continuous optimization scheme for Cyber-Physical System architecture exploration

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