Wenzhong Tang¹, Suresh G. Advani¹

CMES-Computer Modeling in Engineering & Sciences, Vol.8, No.2, pp. 165-176, 2005, DOI:10.3970/cmes.2005.008.165

Abstract An optimization method is proposed to simulate the motion of long flexible fibers in shear flow. The fiber is modeled as spheres connected by massless rigid rods and ball-socket joints. The optimization method is mathematically justified and used to obtain the position of a fiber at the next time step from its current position. Results for a single fiber in simple shear flow agree well with those reported in the literature. The usefulness of the method is demonstrated by simulating the motion of two interactive fibers subjected to shear flow field, and by studying the viscosity of dilute suspensions of… More >

Jie Qu^1,2, Bingye Xu³, Quanlin Jin⁴

CMC-Computers, Materials & Continua, Vol.20, No.2, pp. 119-158, 2010, DOI:10.3970/cmc.2010.020.119

Abstract Large and complex macro-micro coupled constitutive models, which describe metal flow and microstructure evolution during metal forming, are sometimes overparameterized with respect to given sets of experimental datum. This results in poorly identifiable or non-identifiable model parameters. In this paper, a systemic parameter identification method for the large macro-micro coupled constitutive models is proposed. This method is based on the global and local identifiability analysis, in which two identifiability measures are adopted. The first measure accounts for the sensitivity of model results with respect to single parameters, and the second measure accounts for the degree of near-linear dependence of sensitivity… More >

C. Jiang¹, Y.C. Bai¹, X. Han^1,2, H.M. Ning¹

CMC-Computers, Materials & Continua, Vol.18, No.1, pp. 21-42, 2010, DOI:10.3970/cmc.2010.018.021

Abstract In this paper, an efficient interval optimization method based on a reliability-based possibility degree of interval (RPDI) is suggested for the design of uncertain structures. A general nonlinear interval optimization problem is studied in which the objective function and constraints are both nonlinear and uncertain. Through an interval order relation and a reliability-based possibility degree of interval, the uncertain optimization problem is transformed into a deterministic one. A sequence of approximate optimization problems are constructed based on the linear approximation technique. Each approximate optimization problem can be changed to a traditional linear programming problem, which can be easily solved by… More >