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Abstract Computer model calibration typically operates by fine-tuning parameter values in a computer model so that the model output faithfully predicts reality. By using performance targets in place of observed data, we show that calibration techniques can be repurposed for solving multi-objective design problems. Our approach allows us to consider all relevant sources of uncertainty as an integral part of the design process. We demonstrate our proposed approach through both simulation and fine-tuning material design settings to meet performance targets for a wind turbine blade. 

Abstract Calibration of computer models and the use of those design models are two activities traditionally carried out separately. This paper generalizes existing Bayesian inverse analysis approaches for computer model calibration to present a methodology combining calibration and design in a unified Bayesian framework. This provides a computationally efficient means to undertake both tasks while quantifying all relevant sources of uncertainty. Specifically, compared with the traditional approach of design using parameter estimates from previously completed model calibration, this generalized framework inherently includes uncertainty from the calibration process in the design procedure. We demonstrate our approach to the design of a vibration isolation system. We also demonstrate how, when adaptive sampling of the phenomenon of interest is possible, the proposed framework may select new sampling locations using both available real observations and the computer model. This is especially useful when a misspecified model fails to reflect that the calibration parameter is functionally dependent upon the design inputs to be optimized. 

The addition of nanoparticles to a polymer matrix can in certain cases induce a reduction in viscosity, with respect to the pure matrix, in the resulting composites. This counterintuitive phenomenon cannot be explained using the most common rheological models. For this reason, it has been chosen as a good example in this paper to demonstrate the value and methods of dynamic X‐ray and neutron scattering techniques for the investigation of polymer nanocomposites. An overview of the main results on this topic is presented together with an introduction to the basic concepts relating to X‐ray photon correlation spectroscopy, neutron backscattering, and neutron spin echo measurements.

 

Magnesium oxychloride (MOC) excels in performance applications due to inherent structural strength, fire retardant properties, and numerous other attributes. To avoid the slow degradation of MOC when exposed to water, phosphoric acid is usually added, effectively increasing retention of structural properties (water stability). While this is an effective method, it is poorly understood. Additions of 2.5 wt.% and above had positive impacts on the water stability, preserving ~50 wt.% crystalline MOC after water stability tests. Phosphoric acid addition also impacted the reaction kinetics, increasing the activation energy of curing from 72.2 to 87.6‐95.2 kJ/mol. Using synchrotron X‐ray scattering and pair distribution function analysis, we identified an unreported amorphous phase formed when phosphoric acid is added; this phase contains structural motifs related to MgHPO₄·3H₂O (newberyte), Mg₂P₂O₇·3.5H₂O (magnesium pyrophosphate), and amorphous MOC phase. The short‐range order of the samples show a prominent peak at ~3.2 Å that grows with increasing acid addition, believed to be a combination of newberyte (~3.4 Å Mg–P), pyrophosphate (~3.25 Å Mg–P), and MOC (~3.15 Å Mg–Mg). We propose that the increased water stability observed is due to this combined amorphous phase, which retains the low water solubility properties of MgHPO₄·3H₂O and Mg₂P₂O₇·3.5H₂O, effectively protecting the MOC crystalline phase.