Session: Methods for Uncertainty Quantification, Sensitivity Analysis, and Prediction 2

Paper Number: 151983

151983 - Uncertainty Quantification by Probabilistic Analysis of Stirling Engine Performance 

Abstract: 

 

Abstract

A Stirling engine thermodynamic cycle was computationally simulated and probabilistically evaluated in view of the several uncertainties in the performance parameters. Cumulative distribution functions and sensitivity factors were computed for the overall thermal efficiency and net specific power output due to the thermodynamic random variables. These results can be used to quickly identify the most critical design variables in order to optimize the design, enhance performance, increase system availability and make it cost effective. The analysis leads to the selection of the appropriate measurements to be used in the Stirling engine health determination and to the identification of both the most critical measurements and parameters. Probabilistic analysis aims at unifying and improving the control and health monitoring of Stirling engine by increasing the quality and quantity of information available about the engine's health and performance. Conventional engineering design methods are deterministic. The components of a machine are considered as ideal systems and parameter optimizations provide single point estimates of the system response. In reality, many engineering systems are stochastic where a probability assessment of the results is required. Probabilistic engineering design analysis assumes probability distributions of design parameters, instead of mean values only. This enables the designer to design for a specific reliability and hence maximize safety, quality and cost. The approaches for incorporating probabilistic effects in design include the use of factors of safety, the use of the worst case design and the use of probabilistic design.

A stochastic process or a random process in probability is the counterpart to a deterministic process. Instead of dealing with only one possible outcome of how the process might evolve under time, in a stochastic or random process there is some indeterminacy in its future evolution described by probability distributions. This means that even if the initial condition is known, there are many possibilities the process might go to, but some paths may be more probable and others less so.

NESSUS (Numerical Evaluation of Stochastic Structures under Stress) developed by NASA Glenn Research Center. The code combines state of the art probabilistic algorithms with general purpose structural analysis methods to compute the probabilistic response and the reliability of engineering structures. Uncertainty in loading, material properties, geometry, boundary conditions and initial conditions can be simulated. The structural analysis methods include nonlinear finite element methods and boundary element methods. Several probabilistic algorithms are available such as the advanced mean value method and the adaptive importance sampling method. The application of the code includes probabilistic structural response, component and system reliability and risk analysis of structures considering cost of failure. The basic thermodynamic variables are included as random variables along with the mechanical random variables to quantify risk using probabilistic methods to perform sensitivity analysis.

 

 

Presenting Author: Rama Gorla Air Force Institute of Technology

Presenting Author Biography: Dr. Rama Gorla is currently employed as Professor of Aerospace Engineering at the Air Force Institute of Technology, Wright Patterson Air Force Base in Dayton, Ohio. Prior to this, he worked as Professor of Mechanical Engineering and Fenn Distinguished Research Professor at Cleveland State University. He received the Ph.D. degree in Mechanical Engineering from the University of Toledo. His primary research areas are combustion, heat transfer and fluid dynamics. He worked as a turbomachinery design engineer at Teledyne Continental Motors Turbine Engines (TCM-TE) in Toledo, Ohio and as a design engineer of the aerothermodynamics of rotating machinery at Chrysler Corporation in Highland Park, Michigan. Dr. Gorla has published over 700 technical papers in refereed journals and contributed several book chapters in Encyclopedia of Fluid Mechanics. He co-authored two text books: “Turbomachinery” published by Marcel & Dekker Company in 2003 and “Advanced Differential Equations” published by Studera Press in 2016. Professor Gorla is a Fellow of ASME.

Authors:

Rama Gorla Air Force Institute of Technology
John Brewer Air Force Institute of Technology
Abdeel Roman Air Force Research Laboratory