TY - JOUR
T1 - A framework for probabilistic model-based engineering and data synthesis
AU - Ray, Douglas
AU - Ramirez-Marquez, Jose
N1 - Publisher Copyright:
© 2019
PY - 2020/1
Y1 - 2020/1
N2 - Modern computing resources provide scientists, engineers, and system design teams the ability to study phenomena, such as system behavior, in a virtual setting. Computational modeling and simulation (M&S) enables engineers to avoid many of the challenges encountered in traditional design engineering, including the design, manufacture, and testing of expensive prototypes prior to having an optimized design. However, the use of M&S carries its own challenges, such as the computational time and resources required to execute effective studies, and uncertainties arising from simplifying assumptions inherent to computer models, which are intended to be an approximate representation of reality. In recent year advances have been made in a number of areas related to the efficient and reliable use of M&S for system evaluations, including design & analysis of computer experiments, uncertainty quantification, probabilistic analysis, response optimization, and data synthesis techniques. In this review paper, a general framework for systematically executing efficient M&S studies at the component-level, product-level, system-level, and system-of-systems-level is described. A case study is used to demonstrate how statistical and probabilistic techniques can be integrated with M&S to address those challenges inherent to model-based engineering, and how this aligns with the proposed workflow. The example is a gun-launch dynamics model of an artillery projectile developed by US Army engineers, and illustrates the application of this workflow in the study of subsystem system reliability, performance, and end-to-end system-level characterization.
AB - Modern computing resources provide scientists, engineers, and system design teams the ability to study phenomena, such as system behavior, in a virtual setting. Computational modeling and simulation (M&S) enables engineers to avoid many of the challenges encountered in traditional design engineering, including the design, manufacture, and testing of expensive prototypes prior to having an optimized design. However, the use of M&S carries its own challenges, such as the computational time and resources required to execute effective studies, and uncertainties arising from simplifying assumptions inherent to computer models, which are intended to be an approximate representation of reality. In recent year advances have been made in a number of areas related to the efficient and reliable use of M&S for system evaluations, including design & analysis of computer experiments, uncertainty quantification, probabilistic analysis, response optimization, and data synthesis techniques. In this review paper, a general framework for systematically executing efficient M&S studies at the component-level, product-level, system-level, and system-of-systems-level is described. A case study is used to demonstrate how statistical and probabilistic techniques can be integrated with M&S to address those challenges inherent to model-based engineering, and how this aligns with the proposed workflow. The example is a gun-launch dynamics model of an artillery projectile developed by US Army engineers, and illustrates the application of this workflow in the study of subsystem system reliability, performance, and end-to-end system-level characterization.
KW - Calibration
KW - Design of experiments (DOE)
KW - Deterministic computer experiments
KW - Modeling and Simulation (M&S)
KW - Probabilistic optimization
KW - Sensitivity analysis
KW - Space filling designs
KW - Statistical engineering
KW - Trade space
KW - Uncertainty Quantification (UQ)
KW - Validation
KW - Verification
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U2 - 10.1016/j.ress.2019.106679
DO - 10.1016/j.ress.2019.106679
M3 - Review article
AN - SCOPUS:85072764429
SN - 0951-8320
VL - 193
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
M1 - 106679
ER -