TY - JOUR

T1 - Determination of local heat transfer coefficients in precision castings by genetic optimisation aided by numerical simulation

AU - Vasileiou, A. N.

AU - Vosniakos, George Christopher

AU - Pantelis, Dimitrios I.

PY - 2015/3/15

Y1 - 2015/3/15

N2 - The heat transfer coefficient between casting and mould is the most crucial parameter in predicting the evolution of solidification and the resulting properties of the part. In numerical simulations setting, heat transfer coefficient value is considered an ill-posed, inverse problem that requires experimental data, whose solution, if at all possible to reach, is often numerically correct but physically meaningless. In this paper, it is proposed to use a Genetic Algorithm which stochastically explores alternative heat transfer coefficient values. These are evaluated by running a numerical simulation and comparing the resulting cooling curves at a number of nodes of interest to their experimentally measured counterparts until a close enough match is achieved. The combinatorial complexity of determining different heat transfer coefficients corresponding to different regions of complex castings, which are selected due to their differing casting moduli, is possible to tackle in this way with reasonable computational resources. Furthermore, different well-established forms of heat transfer coefficient function can be tried, notably heat transfer coefficient as a function of time (either stepwise or exponential) and as a stepwise function of temperature. Integer encoding in the Genetic Algorithm and a database of accumulating simulation results are features that were developed in order to reduce computational load. The approach is successfully demonstrated on a brass part produced by investment casting, exhibiting three sections of different casting moduli.

AB - The heat transfer coefficient between casting and mould is the most crucial parameter in predicting the evolution of solidification and the resulting properties of the part. In numerical simulations setting, heat transfer coefficient value is considered an ill-posed, inverse problem that requires experimental data, whose solution, if at all possible to reach, is often numerically correct but physically meaningless. In this paper, it is proposed to use a Genetic Algorithm which stochastically explores alternative heat transfer coefficient values. These are evaluated by running a numerical simulation and comparing the resulting cooling curves at a number of nodes of interest to their experimentally measured counterparts until a close enough match is achieved. The combinatorial complexity of determining different heat transfer coefficients corresponding to different regions of complex castings, which are selected due to their differing casting moduli, is possible to tackle in this way with reasonable computational resources. Furthermore, different well-established forms of heat transfer coefficient function can be tried, notably heat transfer coefficient as a function of time (either stepwise or exponential) and as a stepwise function of temperature. Integer encoding in the Genetic Algorithm and a database of accumulating simulation results are features that were developed in order to reduce computational load. The approach is successfully demonstrated on a brass part produced by investment casting, exhibiting three sections of different casting moduli.

KW - Casting

KW - casting modulus

KW - Genetic Algorithm

KW - heat transfer coefficient

KW - numerical simulation

UR - http://www.scopus.com/inward/record.url?scp=84924663827&partnerID=8YFLogxK

U2 - 10.1177/0954406214539468

DO - 10.1177/0954406214539468

M3 - Article

AN - SCOPUS:84924663827

VL - 229

SP - 735

EP - 750

JO - Institution of Mechanical Engineers. Proceedings. Part C: Journal of Mechanical Engineering Science

JF - Institution of Mechanical Engineers. Proceedings. Part C: Journal of Mechanical Engineering Science

SN - 0954-4062

IS - 4

ER -