Spatial variation of heat flux at the metal–mold interface due to mold filling effects in gravity die-casting

Mathematical Modeling of Surface Heat Flux During Quenching

“…The remaining unknown flux components were computed in a similar manner. The inverse algorithm mentioned was used to estimate the interface heat flux in die castings by Prasanna Kumar and Kamath [4] and Arunkumar et al [5] after validating the theoretical model in reference 3.…”

“…[2][3][4][5][6][7][8][9] Prasanna Kumar [3] described a serial solution method for the two-dimensional (2D) inverse heat-conduction problem to estimate multiple heat-flux components and used it to estimate heat-flux components at the metal-mold interface during casting. [4,5] Sarmiento et al estimated the temperature-dependent heat-transfer coefficient during quenching from the measured cooling curves and compared the results of the two computer programs developed for the computation. [6] The heat-flux transients obtained through the inverse technique were more accurate than the Grossmann technique in determining the quench severity of various quenchants; also, it could be used for heattransfer modeling during quenching.…”

Optimization of experimental design for an inverse estimation of the metal-mold heat transfer coefficient in the solidification of Sn–10% Pb

“…This temporally and spatially varying air gap introduces an additional resistance to the heat flow from the metal to the mold. This thermal resistance has a considerable influence in the rate of solidification and thus affects the microstructure formation reported by Arunkumar et al (2008). Once the air gap is formed, the heat transfer across the interface decreases rapidly and a relatively constant value of heat transfer coefficient is obtained; during subsequent stages of solidification a slight drop in the interfacial heat transfer coefficient with time can be observed as Santos et al (2001) indicated in their work.…”

“…Therefore, it is desirable to have linearly-independent sensitivity coefficients, J ij , with large magnitude so that the inverse problem is not very sensitive to measurements errors, and accurate estimations of the parameters can be obtained. The maximization of |F|, called D-optimality method reported by Alifanov et al (1995) and Garcia (1999), is generally aimed to design optimum experiments for the estimation of the unknown parameters, because the confidence region of the estimations is then minimized as Arunkumar et al (2008) reported. Generally, the time-wise variations of sensitivity coefficients and |F| must be examined before attempting to solve the inverse problem.…”

Casting Simulation Methods

“…In another work, Arunkumar et al (34) measured the temperatures inside the vertical wall of a metallic mold at six locations during pouring of an aluminum alloy and solved for the heat flux distribution along the mold-metal interface by inverse heat conduction modeling. The resultant simulated thermal field evolution in the mold wall is given in Figure 3(a).…”