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Accepted Manuscripts

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research-article  
Mark Ewing, Brian Liechty and David Black
J. Verif. Valid. Uncert   doi: 10.1115/1.4041490
Uncertainty quantification is gaining in maturity and importance in engineering analysis. This is especially the case in solid rocket motor applications as designs become increasingly reliant on analysis and less on full-scale testing. While historical motor development programs commonly relied on several full-scale motor firings, modern budget constraints limit the number of available test motors in the design and qualification process. Certain programs have recently been developed with a single qualification motor, and future programs may be developed with no full-scale static test. This puts tremendous importance on both accurate simulations and the quantification of related uncertainty. While historical engineering analysis and design methods have relied heavily on safety factors with built-in conservatism, modern approaches require detailed assessment of reliability to provide optimized and balanced designs. This paper presents methodologies that support the transition toward this type of approach. Fundamental concepts are described that lay the groundwork for uncertainty quantification in general engineering analysis. These include consideration of the sources of uncertainty and their categorization. Of particular importance are the separation of aleatory and epistemic uncertainties and their separate propagation through an uncertainty quantification analysis. This is referred to as a "two-dimensional" approach, and it provides for the assessment of the probability of an occurrence along with the confidence in that prediction. Details of a generalized methodology for uncertainty quantification in this framework are presented, and approaches for interpreting results of uncertainty analyses are described. Simple illustrative examples are presented to help clarify the methodology.
TOPICS: Uncertainty quantification, Motors, Uncertainty, Uncertainty analysis, Separation (Technology), Safety, Reliability, Simulation, Design, Design methodology, Engineering simulation, Testing, Probability, Rockets
research-article  
Matteo Diez, Riccardo Broglia, Danilo Durante, Angelo Olivieri, Emilio F. Campana and Frederick Stern
J. Verif. Valid. Uncert   doi: 10.1115/1.4041372
The objective of the present work is the application of uncertainty quantification (UQ) methods for statistical assessment and validation of experimental and computational ship resistance and motions in irregular head waves, using both time series studies and a stochastic regular wave UQ model solved by a metamodel-based Monte Carlo method. Specifically, UQ methods are used for: (1) statistical assessment and validation of experimental and computational modeling of input irregular waves versus analytical benchmark values; (2) statistical assessment of both experimental and computational ship resistance and motions in irregular waves; (3) validation of computational ship resistance and motions in irregular waves versus experimental benchmark values; (4) statistical validation of both experimental and computational stochastic regular wave UQ model for ship resistance and motions versus irregular-wave experimental benchmark values. Methods for problem (1) include Fourier analysis for wave energy spectrum moments, analysis of the auto-covariance matrix and block-bootstrap methods for the uncertainty of wave elevation statistical moments, along with block-bootstrap methods for the uncertainty of mode and distribution. The uncertainty of wave height statistical estimators is evaluated by the bootstrap method. The same methodologies are used to evaluate statistical uncertainties associated to ship resistance and motions in problem (2). Errors and confidence intervals of statistical estimators are used to define validation criteria in problem (3) and (4). The contribution of the present work is the application and integration of UQ methodologies for the solution of problems from (1) to (4). Results are shown for the Delft catamaran.
research-article  
Matteo Diez, Riccardo Broglia, Danilo Durante, Angelo Olivieri, Emilio F. Campana and Frederick Stern
J. Verif. Valid. Uncert   doi: 10.1115/1.4041372
The objective of the present work is the application of uncertainty quantification (UQ) methods for statistical assessment and validation of experimental and computational ship resistance and motions in irregular head waves, using both time series studies and a stochastic regular wave UQ model solved by a metamodel-based Monte Carlo method. Specifically, UQ methods are used for: (1) statistical assessment and validation of experimental and computational modeling of input irregular waves versus analytical benchmark values; (2) statistical assessment of both experimental and computational ship resistance and motions in irregular waves; (3) validation of computational ship resistance and motions in irregular waves versus experimental benchmark values; (4) statistical validation of both experimental and computational stochastic regular wave UQ model for ship resistance and motions versus irregular-wave experimental benchmark values. Methods for problem (1) include Fourier analysis for wave energy spectrum moments, analysis of the auto-covariance matrix and block-bootstrap methods for the uncertainty of wave elevation statistical moments, along with block-bootstrap methods for the uncertainty of mode and distribution. The uncertainty of wave height statistical estimators is evaluated by the bootstrap method. The same methodologies are used to evaluate statistical uncertainties associated to ship resistance and motions in problem (2). Errors and confidence intervals of statistical estimators are used to define validation criteria in problem (3) and (4). The contribution of the present work is the application and integration of UQ methodologies for the solution of problems from (1) to (4). Results are shown for the Delft catamaran.
TOPICS: Waves, Computational fluid dynamics, Ships, Uncertainty quantification, Uncertainty, Computer simulation, Time series, Wave energy, Errors, Fourier analysis, Monte Carlo methods

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