Dean K. Frederick

Bio: Dean K. Frederick is an academic researcher. The author has contributed to research in topics: Propulsion & Thrust. The author has an hindex of 5, co-authored 6 publications receiving 316 citations.

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

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.

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

Abstract: 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. This paper describes the Commercial Modular Aero Propulsion System Simulation (C-MAPSS), which is representative of a 90,000-lb thrust class two spool, high bypass ratio commercial turbofan engine. A control law resembling the state-of-the-art on board modern aircraft engines is included, consisting of a fan-speed control loop supplemented by relevant engine limit protection regulator loops. The objective of this paper is to provide a top-down overview of the complete engine simulation package.

The Case for Intelligent Propulsion Control for Fast Engine Response

Abstract: Damaged aircraft have occasionally had to rely solely on thrust to maneuver as a consequence of losing hydraulic power needed to operate flight control surfaces. The lack of successful landings in these cases inspired research into more effective methods of utilizing propulsion-only control. That research demonstrated that one of the major contributors to the difficulty in landing is the slow response of the engines as compared to using traditional flight control. To address this, research is being conducted into ways of making the engine more responsive under emergency conditions. This can be achieved by relaxing controller limits, adjusting schedules, and/or redesigning the regulators to increase bandwidth. Any of these methods can enable faster response at the potential expense of engine life and increased likelihood of stall. However, an example sensitivity analysis revealed a complex interaction of the limits and the difficulty in predicting the way to achieve the fastest response. The sensitivity analysis was performed on a realistic engine model, and demonstrated that significantly faster engine response can be achieved compared to standard Bill of Material control. However, the example indicates the need for an intelligent approach to controller limit adjustment in order for the potential to be fulfilled.

User's Guide for the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS): Version 2

Abstract: This report is a Users Guide for version 2 of 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 v.2 has some enhancements over the original, including three actuators rather than one, the addition of actuator and sensor dynamics, and an improved controller, while retaining or improving on the convenience and user-friendliness of the original. C-MAPSS v.2 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.

Engine Parameter Estimation in Test Cells Using Hybrid Physics/Empirical Models

Abstract: Estimation of engine parameters such as thrust in test cells is a difficult process due to the highly nonlinear nature of the engine dynamics, the complex interdependency of thrust and the engine’s health condition, and factors that corrupt thrust measurements due to test stand construction. Because the frequency content of the corrupting dynamics is close to the engine’s dynamics, filtering the thrust signal is not sufficient for extraction of the true dynamic content. A configurable thrust estimation system is developed for accurate data reduction which provides “virtual” measurements of thrust and other necessary parameters at steady state and during aggressive engine transients. The thrust estimation framework consists of a representative nonlinear engine model coupled with an adaptive structural dynamics model. To account for discrepancies between the physics-based model and the true engine, a hybrid model using a novel neural network (NN) enhancement to a physics-based engine model is presented that reduces certain modeling errors between the engine model and the physical plant. This includes engine-to-engine variation, engine degradation and any essential neglected dynamics. To fuse the model and sensor measurements, this hybrid model is used within a constant-gain extended Kalman filter batch estimator which is able to reconstruct the true dynamic performance of the engine using noisy or corrupted sensor measurements and control inputs. The Kalman filter estimates measured and unmeasured parameters and state variables such as engine component deterioration parameters and effective flow areas.Copyright © 2011 by ASME

Damage propagation modeling for aircraft engine run-to-failure simulation

Abstract: This paper describes how damage propagation can be modeled within the modules of aircraft gas turbine engines. To that end, response surfaces of all sensors are generated via a thermo-dynamical simulation model for the engine as a function of variations of flow and efficiency of the modules of interest. An exponential rate of change for flow and efficiency loss was imposed for each data set, starting at a randomly chosen initial deterioration set point. The rate of change of the flow and efficiency denotes an otherwise unspecified fault with increasingly worsening effect. The rates of change of the faults were constrained to an upper threshold but were otherwise chosen randomly. Damage propagation was allowed to continue until a failure criterion was reached. A health index was defined as the minimum of several superimposed operational margins at any given time instant and the failure criterion is reached when health index reaches zero. Output of the model was the time series (cycles) of sensed measurements typically available from aircraft gas turbine engines. The data generated were used as challenge data for the prognostics and health management (PHM) data competition at PHMpsila08.

Direct Remaining Useful Life Estimation Based on Support Vector Regression

Abstract: Prognostics is a major activity in the field of prognostics and health management It aims at increasing the reliability and safety of systems while reducing the maintenance cost by providing an estimate of the current health status and remaining useful life (RUL) Classical RUL estimation techniques are usually composed of different steps: estimations of a health indicator, degradation states, a failure threshold, and finally the RUL In this work, a procedure that is able to estimate the RUL of equipment directly from sensor values without the need for estimating degradation states or a failure threshold is developed A direct relation between sensor values or health indicators is modeled using a support vector regression Using this procedure, the RUL can be estimated at any time instant of the degradation process In addition, an offline wrapper variable selection is applied before training the prediction model This step has a positive impact on the accuracy of the prediction while reducing the computational time compared to existing indirect RUL prediction methods To assess the performance of the proposed approach, the Turbofan dataset, widely considered in the literature, is used Experimental results show that the performance of the proposed method is competitive with other existing approaches

A Data-Level Fusion Model for Developing Composite Health Indices for Degradation Modeling and Prognostic Analysis

Abstract: Prognostics involves the effective utilization of condition or performance-based sensor signals to accurately estimate the remaining lifetime of partially degraded systems and components. The rapid development of sensor technology, has led to the use of multiple sensors to monitor the condition of an engineering system. It is therefore important to develop methodologies capable of integrating data from multiple sensors with the goal of improving the accuracy of predicting remaining lifetime. Although numerous efforts have focused on developing feature-level and decision-level fusion methodologies for prognostics, little research has targeted the development of “data-level” fusion models. In this paper, we present a methodology for constructing a composite health index for characterizing the performance of a system through the fusion of multiple degradation-based sensor data. This methodology includes data selection, data processing, and data fusion steps that lead to an improved degradation-based prognostic model. Our goal is that the composite health index provides a much better characterization of the condition of a system compared to relying solely on data from an individual sensor. Our methodology was evaluated through a case study involving a degradation dataset of an aircraft gas turbine engine that was generated by the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS).

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

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.