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.

Experimental Optimization of the Output Power of a Copper Vapor Laser Using Air as a Buffer Gas

In order to investigate the effect of the pressure buffer gas and frequency on the output power, a copper vapor laser with active medium length of 60 cm and bore of 16 mm has been operated and optimized using air as a buffer gas. The observed oscillatory behavior of the output power versus frequency is in good agreement with the previous reports. The measured results show the maximum output power of ~1.6W at the optimum pressure of 3.8 torr and frequency of 17 kHz. Abundance of the air and reduction of the system volume due to elimination of the gas handling system as well as the economically benefits are the advantages of the employing air as a buffer gas in the copper vapor laser operation.

Optimal design and experimental measurement of the subharmonic characterizations of encapsulated microbubble

在超声域以内基于包含的 microbubbles 的一个理论运动方程， microbubbles 的次谐波描述被一台计算机最佳地设计并且分析帮助设计系统。microbubbles 的次谐波反应上的尺寸，壳弹性和声学的压力的效果理论上被计算为非破坏性的次谐波成像获得最佳的参数。另外，有不同的壳弹性的 microbubbles 被准备，并且他们的次谐波回答在理论计算和好次谐波改进能被与 3m 的吝啬的尺寸使用包含的 microbubbles 获得的声学的测量表演的 vitro.The 结果被测量，它与 material.It 也被显示出的壳的合适的比率从表面活化剂答案被准备最好的操作声学的压力是为非破坏性的次谐波 ima 的 200～400

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.