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User's Guide for the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS)

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TLDR
This report is a Users Guide for the NASA-developed Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) software, which is a transient simulation of a large commercial turbofan engine with a realistic engine control system.
Abstract
This report is a Users Guide for the NASA-developed Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) software, which is a transient simulation of a large commercial turbofan engine (up to 90,000-lb thrust) with a realistic engine control system. The software supports easy access to health, control, and engine parameters through a graphical user interface (GUI). C-MAPSS provides the user with a graphical turbofan engine simulation environment in which advanced algorithms can be implemented and tested. C-MAPSS can run user-specified transient simulations, and it can generate state-space linear models of the nonlinear engine model at an operating point. The code has a number of GUI screens that allow point-and-click operation, and have editable fields for user-specified input. The software includes an atmospheric model which allows simulation of engine operation at altitudes from sea level to 40,000 ft, Mach numbers from 0 to 0.90, and ambient temperatures from -60 to 103 F. The package also includes a power-management system that allows the engine to be operated over a wide range of thrust levels throughout the full range of flight conditions.

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Proceedings ArticleDOI

Validation of an Integrated Airframe and Turbofan Engine Simulation for Evaluation of Propulsion Control Modes

TL;DR: The National Aeronautics and Space Administration (NASA) developed independent airframe and engine models that have been integrated into a single real-time aircraft simulation for piloted evaluation of propulsion control algorithms as mentioned in this paper.
Journal ArticleDOI

Reconstructing secondary test database from PHM08 challenge data set.

TL;DR: A reconstructed database is presented, which provides a robust validation of the model developed for the PHM08 data challenge that would otherwise be ambiguous due to the high-risk of one-time submission.
Journal ArticleDOI

DRES: Data recovery for condition monitoring to enhance system reliability

TL;DR: A data-driven framework for recovering condition data is proposed which is combined by mutual information and Multivariable Linear Regression (MLR), and the correlations among condition monitoring data sets are firstly analysed by mutualInformation and MLR.
Proceedings ArticleDOI

Decision fusion software system for turbine engine fault diagnostics

TL;DR: In this paper, a decision-fusion software system for turbine engines is presented, where a fuzzy logic system is used to predict and diagnose turbine engine health conditions at different levels based on the health parameters, such as efficiency and flow.

Analytic Confusion Matrix Bounds for Fault Detection and Isolation Using a Sum-of-Squared-

TL;DR: This paper performs FDI using sum-of-squared residuals (SSRs), which allows for the generation of optimal sensor sets without numerical simulations, and generates analytic lower, and upper bounds on the confusion matrix elements.
References
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Book

Multivariable Feedback Design

TL;DR: In this article, a comprehensive and unified view of modern multivariate feedback theory and design is presented, where balancing techniques with theory, the objective throughout is to enable the feedback engineer to design real systems.
Book

Multivariable Feedback Design

Maciejowsk
TL;DR: This is the first text to give a comprehensive and unified view of modern multivariable feedback theory and design the feedback engineer to design real systems.
Proceedings ArticleDOI

A Modular Aero-Propulsion System Simulation of a Large Commercial Aircraft Engine

TL;DR: In this article, a simulation of a commercial engine has been developed in a graphical environment to meet the increasing need across the controls and health management community for a common research and development platform.
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