Uncertainty Propagation in Integrated Airframe–Propulsion System Analysis for Hypersonic Vehicles

AbstractIn this paper, a generic full-system estimation software tool is introduced and applied to a data set of actual flight missions to derive a heuristic for system composition for mass and power ratios of considered sub-systems. The capability of evolutionary algorithms to analyse and effectively design spacecraft (sub-)systems is shown. After deriving top-level estimates for each spacecraft sub-system based on heuristic heritage data, a detailed component-based system analysis follows. Various degrees of freedom exist for a hardware-based sub-system design; these are to be resolved via an evolutionary algorithm to determine an optimal system configuration. A propulsion system implementation for a small satellite test case will serve as a reference example of the implemented algorithm application. The propulsion system includes thruster, power processing unit, tank, propellant and general power supply system masses and power consumptions. Relevant performance parameters such as desired thrust, effective exhaust velocity, utilised propellant, and the propulsion type are considered as degrees of freedom. An evolutionary algorithm is applied to the propulsion system scaling model to demonstrate that such evolutionary algorithms are capable of bypassing complex multidimensional design optimisation problems. An evolutionary algorithm is an algorithm that uses a heuristic to change input parameters and a defined selection criterion (e.g., mass fraction of the system) on an optimisation function to refine solutions successively. With sufficient generations and, thereby, iterations of design points, local optima are determined. Using mitigation methods and a sufficient number of seed points, a global optimal system configurations can be found.

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On-board propulsion system analysis of high density propellants

10.2514/6.1998-3670 ◽

1998 ◽

Cited By ~ 3

Author(s):

Steven Schneider

Keyword(s):

System Analysis ◽

High Density ◽

Propulsion System

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A graphical user-interface for propulsion system analysis

10.2514/6.1993-223 ◽

1993 ◽

Author(s):

BRIAN CURLETT ◽

KATHLEEN RYALL

Keyword(s):

User Interface ◽

Graphical User Interface ◽

System Analysis ◽

Propulsion System

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Multiscale Variability and Uncertainty Quantification Based on a Generalized Multiscale Markov Model

Volume 1: 36th Design Automation Conference, Parts A and B ◽

10.1115/detc2010-28790 ◽

2010 ◽

Author(s):

Yan Wang

Keyword(s):

Probability Theory ◽

Bayesian Inference ◽

Markov Model ◽

Uncertainty Quantification ◽

System Analysis ◽

Scale Validation ◽

Uncertainty Propagation ◽

Bayes Rule ◽

Imprecise Probability ◽

Modal Extension

Variability is inherent randomness in systems, whereas uncertainty is due to lack of knowledge. In this paper, a generalized multiscale Markov (GMM) model is proposed to quantify variability and uncertainty simultaneously in multiscale system analysis. The GMM model is based on a new imprecise probability theory that has the form of generalized interval, which is a Kaucher or modal extension of classical set-based intervals to represent uncertainties. The properties of the new definitions of independence and Bayesian inference are studied. Based on a new Bayes’ rule with generalized intervals, three cross-scale validation approaches that incorporate variability and uncertainty propagation are also developed.

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Dynamic Model Identification of Marine Electric Propulsion System

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.503-504.1357 ◽

2012 ◽

Vol 503-504 ◽

pp. 1357-1359

Author(s):

Qiang Wu ◽

Hao Xiong ◽

Guang Wei Meng ◽

Li Bing Zhou

Keyword(s):

Dynamic Model ◽

Electric Propulsion ◽

System Analysis ◽

Model Identification ◽

Propulsion System ◽

Experimental Result ◽

Experiment Data ◽

Electric Propulsion System ◽

Identification Technique ◽

Propulsion Unit

This paper applies identification technique to the marine electric propulsion system analysis, adopts the recursive extended least squares (RELS) algorithm to estimate the structure and parameters of the model, employs the variable forgetting factors into the algorithm to improve the tracking characteristic of the parameters, establishes the dynamic model of a simulated electric propulsion unit under the excitation control based on the experiment data, and finally verifies the validity of the method through the consistency between simulation result and experimental result.

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Probabilistic Framework for Uncertainty Propagation With Both Probabilistic and Interval Variables

Journal of Mechanical Design ◽

10.1115/1.4002720 ◽

2011 ◽

Vol 133 (2) ◽

Cited By ~ 26

Author(s):

Kais Zaman ◽

Mark McDonald ◽

Sankaran Mahadevan

Keyword(s):

System Analysis ◽

Epistemic Uncertainty ◽

Probability Distributions ◽

Uncertainty Propagation ◽

Probabilistic Approach ◽

Interval Data ◽

Cumulative Distribution ◽

System Response ◽

Computationally Efficient ◽

Probabilistic Uncertainty

This paper develops and illustrates a probabilistic approach for uncertainty representation and propagation in system analysis, when the information on the uncertain input variables and/or their distribution parameters may be available as either probability distributions or simply intervals (single or multiple). A unique aggregation technique is used to combine multiple interval data and to compute rigorous bounds on the system response cumulative distribution function. The uncertainty described by interval data is represented through a flexible family of probability distributions. Conversion of interval data to a probabilistic format enables the use of computationally efficient methods for probabilistic uncertainty propagation. Two methods are explored for the implementation of the proposed approach, based on (1) sampling and (2) optimization. The sampling-based strategy is more expensive and tends to underestimate the output bounds. The optimization-based methodology improves both aspects. The proposed methods are used to develop new solutions to challenge problems posed by the Sandia epistemic uncertainty workshop (Oberkampf et al., 2004, “Challenge Problems: Uncertainty in System Response Given Uncertain Parameters,” Reliab. Eng. Syst. Saf., 85, pp. 11–19). Results for the challenge problems are compared with earlier solutions.

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