Engineering topics

Lavelle, Thomas M.

Publications and source records attributed to Lavelle, Thomas M..

33 records · Page 2

NASA NTRS2010

A High-Fidelity Simulation of a Generic Commercial Aircraft Engine and Controller

A new high-fidelity simulation of a generic 40,000 lb thrust class commercial turbofan engine with a representative controller, known as CMAPSS40k, has been developed. Based on dynamic flight test data of a highly instrumented engine and previous engine simulations developed at NASA Glenn Research Center, this non-proprietary simulation was created especially for use in the development of new engine control strategies. C-MAPSS40k is a highly detailed, component-level engine model written in MATLAB/Simulink (The MathWorks, Inc.). Because the model is built in Simulink, users have the ability to use any of the MATLAB tools for analysis and control system design. The engine components are modeled in C-code, which is then compiled to allow faster-than-real-time execution. The engine controller is based on common industry architecture and techniques to produce realistic closed-loop transient responses while ensuring that no safety or operability limits are violated. A significant feature not found in other non-proprietary models is the inclusion of transient stall margin debits. These debits provide an accurate accounting of the compressor surge margin, which is critical in the design of an engine controller. This paper discusses the development, characteristics, and capabilities of the C-MAPSS40k simulation

May, Ryan D.↗

NASA NTRS2006

Intermediate Fidelity Closed Brayton Cycle Power Conversion Model

This paper describes the implementation of an intermediate fidelity model of a closed Brayton Cycle power conversion system (Closed Cycle System Simulation). The simulation is developed within the Numerical Propulsion Simulation System architecture using component elements from earlier models. Of particular interest, and power, is the ability of this new simulation system to initiate a more detailed analysis of compressor and turbine components automatically and to incorporate the overall results into the general system simulation.

Lavelle, Thomas M.↗

NASA NTRS2004

Integrated Control Modeling for Propulsion Systems Using NPSS

The Numerical Propulsion System Simulation (NPSS), an advanced engineering simulation environment used to design and analyze aircraft engines, has been enhanced by integrating control development tools into it. One of these tools is a generic controller interface that allows NPSS to communicate with control development software environments such as MATLAB and EASY5. The other tool is a linear model generator (LMG) that gives NPSS the ability to generate linear, time-invariant state-space models. Integrating these tools into NPSS enables it to be used for control system development. This paper will discuss the development and integration of these tools into NPSS. In addition, it will show a comparison of transient model results of a generic, dual-spool, military-type engine model that has been implemented in NPSS and Simulink. It will also show the linear model generator s ability to approximate the dynamics of a nonlinear NPSS engine model.

Parker, Khary I.↗

NASA NTRS2003

Neural Network and Regression Methods Demonstrated in the Design Optimization of a Subsonic Aircraft

The neural network and regression methods of NASA Glenn Research Center s COMETBOARDS design optimization testbed were used to generate approximate analysis and design models for a subsonic aircraft operating at Mach 0.85 cruise speed. The analytical model is defined by nine design variables: wing aspect ratio, engine thrust, wing area, sweep angle, chord-thickness ratio, turbine temperature, pressure ratio, bypass ratio, fan pressure; and eight response parameters: weight, landing velocity, takeoff and landing field lengths, approach thrust, overall efficiency, and compressor pressure and temperature. The variables were adjusted to optimally balance the engines to the airframe. The solution strategy included a sensitivity model and the soft analysis model. Researchers generated the sensitivity model by training the approximators to predict an optimum design. The trained neural network predicted all response variables, within 5-percent error. This was reduced to 1 percent by the regression method. The soft analysis model was developed to replace aircraft analysis as the reanalyzer in design optimization. Soft models have been generated for a neural network method, a regression method, and a hybrid method obtained by combining the approximators. The performance of the models is graphed for aircraft weight versus thrust as well as for wing area and turbine temperature. The regression method followed the analytical solution with little error. The neural network exhibited 5-percent maximum error over all parameters. Performance of the hybrid method was intermediate in comparison to the individual approximators. Error in the response variable is smaller than that shown in the figure because of a distortion scale factor. The overall performance of the approximators was considered to be satisfactory because aircraft analysis with NASA Langley Research Center s FLOPS (Flight Optimization System) code is a synthesis of diverse disciplines: weight estimation, aerodynamic analysis, engine cycle analysis, propulsion data interpolation, mission performance, airfield length for landing and takeoff, noise footprint, and others.

Hopkins, Dale A.↗

NASA NTRS2000

Mean Line Pump Flow Model in Rocket Engine System Simulation

A mean line pump flow modeling method has been developed to provide a fast capability for modeling turbopumps of rocket engines. Based on this method, a mean line pump flow code PUMPA has been written that can predict the performance of pumps at off-design operating conditions, given the loss of the diffusion system at the design point. The pump code can model axial flow inducers, mixed-flow and centrifugal pumps. The code can model multistage pumps in series. The code features rapid input setup and computer run time, and is an effective analysis and conceptual design tool. The map generation capability of the code provides the map information needed for interfacing with a rocket engine system modeling code. The off-design and multistage modeling capabilities of the code permit parametric design space exploration of candidate pump configurations and provide pump performance data for engine system evaluation. The PUMPA code has been integrated with the Numerical Propulsion System Simulation (NPSS) code and an expander rocket engine system has been simulated. The mean line pump flow code runs as an integral part of the NPSS rocket engine system simulation and provides key pump performance information directly to the system model at all operating conditions.

Veres, Joseph P.↗

NASA NTRS2000

Lessons Learned During Solutions of Multidisciplinary Design Optimization Problems

Optimization research at NASA Glenn Research Center has addressed the design of structures, aircraft and airbreathing propulsion engines. During solution of the multidisciplinary problems several issues were encountered. This paper lists four issues and discusses the strategies adapted for their resolution: (1) The optimization process can lead to an inefficient local solution. This deficiency was encountered during design of an engine component. The limitation was overcome through an augmentation of animation into optimization. (2) Optimum solutions obtained were infeasible for aircraft and air-breathing propulsion engine problems. Alleviation of this deficiency required a cascading of multiple algorithms. (3) Profile optimization of a beam produced an irregular shape. Engineering intuition restored the regular shape for the beam. (4) The solution obtained for a cylindrical shell by a subproblem strategy converged to a design that can be difficult to manufacture. Resolution of this issue remains a challenge. The issues and resolutions are illustrated through six problems: (1) design of an engine component, (2) synthesis of a subsonic aircraft, (3) operation optimization of a supersonic engine, (4) design of a wave-rotor-topping device, (5) profile optimization of a cantilever beam, and (6) design of a cvlindrical shell. The combined effort of designers and researchers can bring the optimization method from academia to industry.

Patnaik, Suna N.↗

NASA NTRS2000

Cascade Optimization for Aircraft Engines With Regression and Neural Network Analysis - Approximators

The NASA Engine Performance Program (NEPP) can configure and analyze almost any type of gas turbine engine that can be generated through the interconnection of a set of standard physical components. In addition, the code can optimize engine performance by changing adjustable variables under a set of constraints. However, for engine cycle problems at certain operating points, the NEPP code can encounter difficulties: nonconvergence in the currently implemented Powell's optimization algorithm and deficiencies in the Newton-Raphson solver during engine balancing. A project was undertaken to correct these deficiencies. Nonconvergence was avoided through a cascade optimization strategy, and deficiencies associated with engine balancing were eliminated through neural network and linear regression methods. An approximation-interspersed cascade strategy was used to optimize the engine's operation over its flight envelope. Replacement of Powell's algorithm by the cascade strategy improved the optimization segment of the NEPP code. The performance of the linear regression and neural network methods as alternative engine analyzers was found to be satisfactory. This report considers two examples-a supersonic mixed-flow turbofan engine and a subsonic waverotor-topped engine-to illustrate the results, and it discusses insights gained from the improved version of the NEPP code.

Patnaik, Surya N.↗

NASA NTRS1999

Neural Network and Regression Approximations Used in Aircraft Design

NASA Lewis Research Center's CometBoards Test Bed was used to create regression and neural network models for a High-Speed Civil Transport (HSCT) aircraft. Both approximation models that replaced the actual analysis tool predicted the aircraft response in a trivial computational effort. The models allow engineers to quickly study the effects of design variables on constraint and objective values for a given aircraft configuration. For example, an engineer can change the engine size by 1000 pounds of thrust and quickly see how this change affects all the output values without rerunning the entire simulation. In addition, an engineer can change a constraint and use the approximation models to quickly reoptimize the configuration. Generating the neural network and the regression models is a time-consuming process, but this exercise has to be carried out only once. Furthermore, an automated process can reduce calculations substantially.

Patnaik, Surya N.↗

NASA NTRS1998

Neural Network and Regression Approximations in High Speed Civil Transport Aircraft Design Optimization

Nonlinear mathematical-programming-based design optimization can be an elegant method. However, the calculations required to generate the merit function, constraints, and their gradients, which are frequently required, can make the process computational intensive. The computational burden can be greatly reduced by using approximating analyzers derived from an original analyzer utilizing neural networks and linear regression methods. The experience gained from using both of these approximation methods in the design optimization of a high speed civil transport aircraft is the subject of this paper. The Langley Research Center's Flight Optimization System was selected for the aircraft analysis. This software was exercised to generate a set of training data with which a neural network and a regression method were trained, thereby producing the two approximating analyzers. The derived analyzers were coupled to the Lewis Research Center's CometBoards test bed to provide the optimization capability. With the combined software, both approximation methods were examined for use in aircraft design optimization, and both performed satisfactorily. The CPU time for solution of the problem, which had been measured in hours, was reduced to minutes with the neural network approximation and to seconds with the regression method. Instability encountered in the aircraft analysis software at certain design points was also eliminated. On the other hand, there were costs and difficulties associated with training the approximating analyzers. The CPU time required to generate the input-output pairs and to train the approximating analyzers was seven times that required for solution of the problem.

Patniak, Surya N.↗

NASA NTRS1996

Optimization of Air-Breathing Engine Concept

The design optimization of air-breathing propulsion engine concepts has been accomplished by soft-coupling the NASA Engine Performance Program (NEPP) analyzer with the NASA Lewis multidisciplinary optimization tool COMETBOARDS. Engine problems, with their associated design variables and constraints, were cast as nonlinear optimization problems with thrust as the merit function. Because of the large number of mission points in the flight envelope, the diversity of constraint types, and the overall distortion of the design space; the most reliable optimization algorithm available in COMETBOARDS, when used by itself, could not produce satisfactory, feasible, optimum solutions. However, COMETBOARDS' unique features-which include a cascade strategy, variable and constraint formulations, and scaling devised especially for difficult multidisciplinary applications-successfully optimized the performance of subsonic and supersonic engine concepts. Even when started from different design points, the combined COMETBOARDS and NEPP results converged to the same global optimum solution. This reliable and robust design tool eliminates manual intervention in the design of air-breathing propulsion engines and eases the cycle analysis procedures. It is also much easier to use than other codes, which is an added benefit. This paper describes COMETBOARDS and its cascade strategy and illustrates the capabilities of the combined design tool through the optimization of a high-bypass- turbofan wave-rotor-topped subsonic engine and a mixed-flow-turbofan supersonic engine.

Patnaik, Surya N.↗

NASA NTRS1996

Cascade Optimization Strategy for Aircraft and Air-Breathing Propulsion System Concepts

Design optimization for subsonic and supersonic aircraft and for air-breathing propulsion engine concepts has been accomplished by soft-coupling the Flight Optimization System (FLOPS) and the NASA Engine Performance Program analyzer (NEPP), to the NASA Lewis multidisciplinary optimization tool COMETBOARDS. Aircraft and engine design problems, with their associated constraints and design variables, were cast as nonlinear optimization problems with aircraft weight and engine thrust as the respective merit functions. Because of the diversity of constraint types and the overall distortion of the design space, the most reliable single optimization algorithm available in COMETBOARDS could not produce a satisfactory feasible optimum solution. Some of COMETBOARDS' unique features, which include a cascade strategy, variable and constraint formulations, and scaling devised especially for difficult multidisciplinary applications, successfully optimized the performance of both aircraft and engines. The cascade method has two principal steps: In the first, the solution initiates from a user-specified design and optimizer, in the second, the optimum design obtained in the first step with some random perturbation is used to begin the next specified optimizer. The second step is repeated for a specified sequence of optimizers or until a successful solution of the problem is achieved. A successful solution should satisfy the specified convergence criteria and have several active constraints but no violated constraints. The cascade strategy available in the combined COMETBOARDS, FLOPS, and NEPP design tool converges to the same global optimum solution even when it starts from different design points. This reliable and robust design tool eliminates manual intervention in the design of aircraft and of air-breathing propulsion engines where it eases the cycle analysis procedures. The combined code is also much easier to use, which is an added benefit. This paper describes COMETBOARDS and its cascade strategy and illustrates the capability of the combined design tool through the optimization of a subsonic aircraft and a high-bypass-turbofan wave-rotor-topped engine.

Patnaik, Surya N.↗

NASA NTRS1995

Enhanced capabilities and modified users manual for axial-flow compressor conceptual design code CSPAN

Modifications made to the axial-flow compressor conceptual design code CSPAN are documented in this report. Endwall blockage and stall margin predictions were added. The loss-coefficient model was upgraded. Default correlations for rotor and stator solidity and aspect-ratio inputs and for stator-exit tangential velocity inputs were included in the code along with defaults for aerodynamic design limits. A complete description of input and output along with sample cases are included.

Glassman, Arthur J.↗

NASA NTRS1994

Graphical User Interface for the NASA FLOPS Aircraft Performance and Sizing Code

XFLOPS is an X-Windows/Motif graphical user interface for the aircraft performance and sizing code FLOPS. This new interface simplifies entering data and analyzing results, thereby reducing analysis time and errors. Data entry is simpler because input windows are used for each of the FLOPS namelists. These windows contain fields to input the variable's values along with help information describing the variable's function. Analyzing results is simpler because output data are displayed rapidly. This is accomplished in two ways. First, because the output file has been indexed, users can view particular sections with the click of a mouse button. Second, because menu picks have been created, users can plot engine and aircraft performance data. In addition, XFLOPS has a built-in help system and complete on-line documentation for FLOPS.

Lavelle, Thomas M.↗

NASA NTRS1992

Concurrent optimization of airframe and engine design parameters

An integrated system for the multidisciplinary analysis and optimization of airframe and propulsion design parameters is being developed. This system is known as IPAS, the Integrated Propulsion/Airframe Analysis System. The traditional method of analysis is one in which the propulsion system analysis is loosely coupled to the overall mission performance analysis. This results in a time consuming iterative process. First, the engine is designed and analyzed. Then, the results from this analysis are used in a mission analysis to determine the overall aircraft performance. The results from the mission analysis are used as a guide as the engine redesigned and the entire process repeated. In IPAS, the propulsion system, airframe, and mission are closely coupled. The propulsion system analysis code is directly integrated into the mission analysis code. This allows the propulsion design parameters to be optimized along with the airframe and mission design parameters, significantly reducing the time required to obtain an optimized solution.

Lavelle, Thomas M.↗

NASA NTRS1991

Concurrent optimization of airframe and engine design parameters

An integrated system for the multidisciplinary analysis and optimization of airframe and propulsion design parameters is being developed. This system is known as IPAS, the Integrated Propulsion/Airframe Analysis System. The traditional method of analysis is one in which the propulsion system analysis is loosely coupled to the overall mission performance analysis. This results in a time consuming iterative process. First, the engine is designed and analyzed. Then, the results from this analysis are used in a mission analysis to determine the overall aircraft performance. The results from the mission analysis are used as a guide as the engine is redesigned and the entire process repeated. In IPAS, the propulsion system, airframe, and mission are closely coupled. The propulsion system analysis code is directly integrated into the mission analysis code. This allows the propulsion design parameters to be optimized along with the airframe and mission design parameters, significantly reducing the time required to obtain an optimized solution.

Lavelle, Thomas M.↗