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Books > Professional & Technical > Mechanical engineering & materials > Production engineering > Reliability engineering
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BERRU Predictive Modeling - Best Estimate Results with Reduced Uncertainties (Hardcover, 1st ed. 2019)
Loot Price: R4,833
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BERRU Predictive Modeling - Best Estimate Results with Reduced Uncertainties (Hardcover, 1st ed. 2019)
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This book addresses the experimental calibration of best-estimate
numerical simulation models. The results of measurements and
computations are never exact. Therefore, knowing only the nominal
values of experimentally measured or computed quantities is
insufficient for applications, particularly since the respective
experimental and computed nominal values seldom coincide. In the
author's view, the objective of predictive modeling is to extract
"best estimate" values for model parameters and predicted results,
together with "best estimate" uncertainties for these parameters
and results. To achieve this goal, predictive modeling combines
imprecisely known experimental and computational data, which calls
for reasoning on the basis of incomplete, error-rich, and
occasionally discrepant information. The customary methods used for
data assimilation combine experimental and computational
information by minimizing an a priori, user-chosen, "cost
functional" (usually a quadratic functional that represents the
weighted errors between measured and computed responses). In
contrast to these user-influenced methods, the BERRU (Best Estimate
Results with Reduced Uncertainties) Predictive Modeling methodology
developed by the author relies on the thermodynamics-based maximum
entropy principle to eliminate the need for relying on minimizing
user-chosen functionals, thus generalizing the "data adjustment"
and/or the "4D-VAR" data assimilation procedures used in the
geophysical sciences. The BERRU predictive modeling methodology
also provides a "model validation metric" which quantifies the
consistency (agreement/disagreement) between measurements and
computations. This "model validation metric" (or "consistency
indicator") is constructed from parameter covariance matrices,
response covariance matrices (measured and computed), and response
sensitivities to model parameters. Traditional methods for
computing response sensitivities are hampered by the "curse of
dimensionality," which makes them impractical for applications to
large-scale systems that involve many imprecisely known parameters.
Reducing the computational effort required for precisely
calculating the response sensitivities is paramount, and the
comprehensive adjoint sensitivity analysis methodology developed by
the author shows great promise in this regard, as shown in this
book. After discarding inconsistent data (if any) using the
consistency indicator, the BERRU predictive modeling methodology
provides best-estimate values for predicted parameters and
responses along with best-estimate reduced uncertainties (i.e.,
smaller predicted standard deviations) for the predicted
quantities. Applying the BERRU methodology yields optimal,
experimentally validated, "best estimate" predictive modeling tools
for designing new technologies and facilities, while also improving
on existing ones.
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