Numerical simulations of different ceramic production phases often involve complex constitutive models, with difficult calibration process, relying on a large number of experiments. Methodological developments, proposed in present paper regarding this calibration problem can be outlined as follows: assessment of constitutive parameters is performed through inverse analysis procedure, centered on minimization of discrepancy function which quantifies the difference between measurable quantities and their computed counterpart. Resulting minimization problem is solved through genetic algorithms, while the computational burden is made consistent with constraints of routine industrial applications by exploiting Reduced Order Model (ROM) based on proper orthogonal decomposition. Throughout minimization, a gradual enrichment of designed ROM is used, by including additional simulations. Such strategy turned out to be beneficial when applied to models with a large number of parameters. Developed procedure seems to be effective when dealing with complex constitutive models, that can give rise to non-continuous discrepancy function due to the numerical instabilities. Proposed approach is tested and experimentally validated on the calibration of modified Drucker-Prager CAP model, frequently adopted for ceramic powder pressing simulations. Assessed values are compared with those obtained by traditional, timeconsuming tests, performed on pressed green bodies.
Indentation test is used with growing popularity for the characterization of various materials on different scales. Developed methods are combining the test with computer simulation and inverse analyses to assess material parameters entering into constitutive models. The outputs of such procedures are expressed as evaluation of sought parameters in deterministic sense, while for engineering practice it is desirable to assess also the uncertainty which affects the final estimates resulting from various sources of errors within the identification procedure. In this paper an experimentalnumerical method is presented centered on inverse analysis build upon data collected from the indentation test in the form of force-penetration relationship (so-called 'indentation curve'). Recursive simulations are made computationally economical by an 'a priori' model reduction procedure. Resulting inverse problem is solved in a stochastic context using Monte Carlo simulations and non-sequential Extended Kalman filter.Obtained results are presented comparatively as for accuracy and computational efficiency.
This review gives a broad introduction to nanotechnology, mesoporous silica nanoparticles (MSNs) and synthesis techniques, along with their applications. Recent advances in morphological control and surface functionalization of MSNs have improved their biocompatibility and a strong emphasis on the physicochemical characteristics of MSNs, resulting in a step forward in traditional intervention techniques. This review highlights recent improvements in silica-assisted drug delivery systems including MSN-based sustained drug delivery systems and MSN-based controlled, targeted drug delivery systems. Silica nanoparticles can be used to blend different materials, mix different functions and be a cornerstone for a multifunctional nanomedicine podium for multimodal imaging and diagnostics therapy.
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