Optimal Experiment Design for the Identification of the Interfacial Heat Transfer Coefficient in Sand Casting

The interfacial heat transfer coefficient(IHTC)is one of the main input parameters required by casting simulation software.It plays an important role in the accurate modeling of the solidification process.However,its value is not easily identifiable by means of experimental methods requiring temperature measurements during the solidification process itself.For these reasons,an optimal experiment design was performed in this study to determine the optimal position for the temperature measurement and the optimal thickness of the rectangular cast iron part.This parameter was identified using an inverse technique.In particular,two different algorithms were used:Levenberg Marquard(LM)and Monte Carlo(MC).A numerical model of the solidification process was associated with the optimization algorithm.The temperature was measured at different positions from the mould/metal interface at d=0 mm(mould/metal interface),30 mm,60 mm and 90 mm.the thicknesses of the cast part were:L1=40 mm,60 mm and 80 mm.A comparative study on the IHTC identification was then carried out by varying the initial value of the IHTC between 500 Wm^(-2)K^(-1) and 1050 Wm^(-2)K^(-1).Results showed that the MC algorithm used for estimating the IHTC gives the best results,and the optimal position was at d=30 mm,the position closest to the mould/metal interface,for the lowest thickness L1=40 mm.

A Multi-Objective Optimal Experimental Design Framework for Enhancing the Efficiency of Online Model Identification Platforms

Recent advances in automation and digitization enable the close integration of physical devices with their virtual counterparts, facilitating the real-time modeling and optimization of a multitude of processes in an automatic way. The rich and continuously updated data environment provided by such systems makes it possible for decisions to be made over time to drive the process toward optimal targets. In many man- ufacturing processes, in order to achieve an overall optimal process, the simultaneous assessment of mul- tiple objective functions related to process performance and cost is necessary. In this work, a multi- objective optimal experimental design framework is proposed to enhance the ef ciency of online model-identi cation platforms. The proposed framework permits exibility in the choice of trade-off experimental design solutions, which are calculated online that is, during the execution of experiments. The application of this framework to improve the online identi cation of kinetic models in ow reactors is illustrated using a case study in which a kinetic model is identi ed for the esteri cation of benzoic acid and ethanol in a microreactor.

Combined Size and Shape Optimization of Structures with DOE,RSM and GA

In this paper,size and shape optimization problem of a machine gun system is addressed with an efficient hybrid method,in which a novel and flexible mesh morphing technique is employed to achieve fast parameterization and modification of complexity structure without going back to CAD for reconstruction of geometric models or to finite element analysis（ FEA） for remodeling. Design of experiments（ DOE） and response surface method（ RSM） are applied to approximate the constitutive parameters of a machine gun system based on experimental tests. Further FEA,secondary development technique and genetic algorithm（ GA） are introduced to find all the optimal solutions in one go and the optimal design of the demonstrated machine gun system is obtained. Results of the rigid-flexible coupling dynamic analysis and exterior ballistics calculation validate the proposed methodology,which is relatively time-saving,reliable and has the potential to solve similar problems.

Improved dynamic grey wolf optimizer

In the standard grey wolf optimizer(GWO), the search wolf must wait to update its current position until the comparison between the other search wolves and the three leader wolves is completed. During this waiting period, the standard GWO is seen as the static GWO. To get rid of this waiting period, two dynamic GWO algorithms are proposed: the first dynamic grey wolf optimizer(DGWO1) and the second dynamic grey wolf optimizer(DGWO2). In the dynamic GWO algorithms, the current search wolf does not need to wait for the comparisons between all other search wolves and the leading wolves, and its position can be updated after completing the comparison between itself or the previous search wolf and the leading wolves. The position of the search wolf is promptly updated in the dynamic GWO algorithms, which increases the iterative convergence rate. Based on the structure of the dynamic GWOs, the performance of the other improved GWOs is examined, verifying that for the same improved algorithm, the one based on dynamic GWO has better performance than that based on static GWO in most instances.

An experiment for obtaining DOP ellipsoid using particle swarm optimization algorithm

The degree of polarization (DOP) ellipsoid can be used as either feedback or feedforward signal for automatic polarization mode dispersion compensation. We have realized the experiment for obtaining DOP ellipsoid from 100 sampling data of output states of polarization using particle swarm optimization (PSO) as ellipsoid data fitting algorithm. It was shown that the PSO algorithm was powerful for ellipsoid data fitting with high precision within 250 ms.