Integrated Tools for Future Distributed Engine Control Technologies

Culley, Dennis E.; Thomas, Randy; Saus, Joseph R.

doi:10.1115/gt2013-95118

Cited by 3 publications

(3 citation statements)

References 19 publications

(3 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The test bench is also being used to integrate with NASA-developed distributed engine control technologies. 23 Data was obtained from the WESTT simulation at several cruise operating points for comparisons to the developed nonlinear engine model. The WESTT test bench provides simulation output for all of the available sensors on the DGEN 380 engine with the addition of pressures, temperatures, and mass flow rates at all engine component locations.…”

Section: B Price Induction Simulation -Westt Test Benchmentioning

confidence: 99%

Propulsion Controls Modeling for a Small Turbofan Engine

Connolly

Csank²,

Chicatelli³

et al. 2017

53rd AIAA/SAE/ASEE Joint Propulsion Conference

View full text Add to dashboard Cite

A nonlinear dynamic model and propulsion controller are developed for a small-scale turbofan engine. The small-scale turbofan engine is based on the Price Induction company's DGEN 380, one of the few turbofan engines targeted for the personal light jet category. Comparisons of the nonlinear dynamic turbofan engine model to actual DGEN 380 engine test data and a Price Induction simulation are provided. During engine transients, the nonlinear model typically agrees within 10% error, even though the nonlinear model was developed from limited available engine data. A gain scheduled proportional integral low speed shaft controller with limiter safety logic is created to replicate the baseline DGEN 380 controller. The new controller provides desired gain and phase margins and is verified to meet Federal Aviation Administration transient propulsion system requirements. In understanding benefits, there is a need to move beyond simulation for the demonstration of advanced control architectures and technologies by using real-time systems and hardware. The small-scale DGEN 380 provides a cost effective means to accomplish advanced controls testing on a relevant turbofan engine platform.

show abstract

Section: B Price Induction Simulation -Westt Test Benchmentioning

confidence: 99%

Propulsion Controls Modeling for a Small Turbofan Engine

Connolly

Csank²,

Chicatelli³

et al. 2017

53rd AIAA/SAE/ASEE Joint Propulsion Conference

View full text Add to dashboard Cite

show abstract

“…Control design, testing, and implementation are often the final steps in the engine development process and are therefore subject to strict time and budget constraints. 8 The high risk of developing new technologies and implementing new ideas tends to impede progress toward alternative control architectures over such a limited time frame. Having the ability to conceptualize and develop the system architecture independent of a physical engine prototype would allow for relaxation of many of these project constraints, particularly that of time, leading to implementation of distributed engine control in an aerospace application.…”

Section: Introductionmentioning

confidence: 99%

“…Having the ability to conceptualize and develop the system architecture independent of a physical engine prototype would allow for relaxation of many of these project constraints, particularly that of time, leading to implementation of distributed engine control in an aerospace application. 8 A hardware-in-the-loop (HIL) system is under development at NASA Glenn Research Center, as part of the Distributed Engine Control (DEC) task, which aims to provide a simulation environment for testing control architectures and hardware without the need for a physical engine. This effectively allows the control design to develop in parallel with engine design.…”

Section: Introductionmentioning

confidence: 99%

A Modular Framework for Modeling Hardware Elements in Distributed Engine Control Systems

Zinnecker¹,

Culley²,

Aretskin-Hariton³

2014

50th AIAA/ASME/SAE/ASEE Joint Propulsion Conference

View full text Add to dashboard Cite

Progress toward the implementation of distributed engine control in an aerospace application may be accelerated through the development of a hardware-in-the-loop (HIL) system for testing new control architectures and hardware outside of a physical test cell environment. One component required in an HIL simulation system is a high-fidelity model of the control platform: sensors, actuators, and the control law. The control system developed for the Commercial Modular Aero-Propulsion System Simulation 40k (C-MAPSS40k) provides a verifiable baseline for development of a model for simulating a distributed control architecture. This distributed controller model will contain enhanced hardware models, capturing the dynamics of the transducer and the effects of data processing, and a model of the controller network. A multilevel framework is presented that establishes three sets of interfaces in the control platform: communication with the engine (through sensors and actuators), communication between hardware and controller (over a network), and the physical connections within individual pieces of hardware. This introduces modularity at each level of the model, encouraging collaboration in the development and testing of various control schemes or hardware designs. At the hardware level, this modularity is leveraged through the creation of a Simulink R library containing blocks for constructing smart transducer models complying with the IEEE 1451 specification. These hardware models were incorporated in a distributed version of the baseline C-MAPSS40k controller and simulations were run to compare the performance of the two models. The overall tracking ability differed only due to quantization effects in the feedback measurements in the distributed controller. Additionally, it was also found that the added complexity of the smart transducer models did not prevent real-time operation of the distributed controller model, a requirement of an HIL system. NomenclatureVariables N shaft speed P pressure P 50 pressure at station 50 (exit of low-pressure turbine) T temperature T s update rate/sampling time W flow rate (·) sens sensed variable * Control Systems Engineer, alicia.m.zinnecker@nasa.gov, AIAA Member. † Research Engineer, dennis.e.culley@nasa.gov, AIAA Senior Member.

show abstract