Calculation of thermophysical properties of carbon and low alloyed steels for modeling of solidification processes

Miettinen, Jyrki; Louhenkilpi, Seppo

doi:10.1007/bf02662773

Cited by 54 publications

(42 citation statements)

References 9 publications

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“…3 kg m − . The thermal conductivity and specific heat for individual phases reported in the literature depend of the authors, although follows similar trends [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][24][25][26][27][28][29][30][31][32][33] . The reason for such lack is due to the possibility of different features of the phases formed depending on the thermal history.…”

Section: Thermophysical Propertiesmentioning

confidence: 72%

“…For this purpose, it was necessary to predict the temperature field coupled dynamically with the welding evolution and the material thermophysical properties, together with the kinetic model for phase transformations, besides the model for hardness prediction. In order to model the process, the phenomena of heat transfer by radiation, convection and conduction are taken into account coupled with mass transfer, melting and solidification and the thermophysical properties were assumed as composition and temperature dependent [20][21][22][23][24][25] . The energy equation for a general coordinate system is represented in compact form by the Equation 1 [17][18][19] .…”

Section: Model Featuresmentioning

confidence: 99%

“…The material properties were considered as temperature and composition dependent 4,20,24,25 . The heat capacity and thermal conductivity are presented in Figures 2 and 3 respectively.…”

Section: Thermophysical Propertiesmentioning

confidence: 99%

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Numerical Predictions for the Thermal History, Microstructure and Hardness Distributions at the HAZ during Welding of Low Alloy Steels

et al. 2016

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A phenomenological model to predict the multiphase diffusional decomposition of the austenite in low-alloy hypoeutectoid steels was adapted for welding conditions. The kinetics of phase transformations coupled with the heat transfer phenomena was numerically implemented using the Finite Volume Method (FVM) in a computational code. The model was applied to simulate the welding of a commercial type of low-alloy hypoeutectoid steel, making it possible to track the phase formations and to predict the volume fractions of ferrite, pearlite and bainite at the heat-affected zone (HAZ). The volume fraction of martensite was calculated using a novel kinetic model based on the optimization of the well-known Koistinen-Marburger model. Results were confronted with the predictions provided by the continuous cooling transformation (CCT) diagram for the investigated steel, allowing the use of the proposed methodology for the microstructure and hardness predictions at the HAZ of low-alloy hypoeutectoid steels.

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Section: Thermophysical Propertiesmentioning

confidence: 72%

Section: Model Featuresmentioning

confidence: 99%

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Numerical Predictions for the Thermal History, Microstructure and Hardness Distributions at the HAZ during Welding of Low Alloy Steels

et al. 2016

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“…Steel chemistry was taken into account in these detailed simulations, because it is known to have an effect on the Niyama criterion. The effects of chemistry on the thermophysical properties of the steel, including the liquidus and solidus temperatures ( l T and s T , respectively), were computed using the interdendritic solidification computer software (IDS) developed by Miettinen et al [25,26] The variation with steel chemistry of the Niyama criterion results is due primarily to changes in the 2 Although MAGMASOFT [27] was used in this work to simulate the casting trials, a number of simulation packages are available, and most of them are capable of calculating the Niyama criterion. In fact, the authors recently performed a comparison between MAGMASOFT and AFSolid, [28] and determined that the Niyama values calculated by these two packages for the same casting conditions are similar, provided that one takes care to ensure the Niyama values are calculated in the same manner (e.g., evaluated at the same temperature), and that the values are converted to the same units.…”

Section: Simulation Of Casting Trialsmentioning

confidence: 99%

Yield Improvement in Steel Casting (Yield II)

Hardin¹,

Beckermann²,

Hays³

2002

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In 1973, the Steel Founders' Society of America (SFSA) published Risering Steel Castings [1], a foundry handbook intended to provide risering guidelines for use in steel foundries. The guidelines contained in Risering Steel Castings were developed based on experimental casting work and supported by computer simulations. The steel casting industry has been well served by this handbook since its publication; following the guidelines generally results in sound castings. However, examples have surfaced in the last quarter of a century that indicate that the riser feeding distance rules contained in Risering Steel Castings are overly conservative in certain instances, especially where results have been extrapolated. Conservative feeding distance rules lead to the use of more risers than necessary on a casting, which in turn leads to a reduction in casting yield.Beginning in the mid-1990's, a research effort was undertaken to develop a new set of risering guidelines [2, 3]. This research was based on an extensive set of low-alloy steel plate casting trials performed at various foundries throughout North America. A wide variety of plate dimensions were utilized in the trials, which produced castings ranging from radiographically sound to ASTM shrinkage x-ray level 5. Casting conditions (alloy composition, mold material, superheat, pouring time, etc.) were recorded by each foundry for each plate cast, and this information was then utilized to numerically simulate the casting of each plate, using modern casting simulation software. Once it was determined that there was good agreement between the casting trial results and their corresponding simulations, a large number of simulations were performed for geometries and/or casting conditions that were not used in the casting trials, thus producing a more complete data set. By analyzing all of this data, a new set of feeding distance rules for sound castings was developed. These new rules are presented in this publication. Differences between this work and Risering Steel Castings:• Usually less conservative feeding distances: The feeding distances calculated using the guidelines presented here are similar to those calculated using Risering Steel Castings in some instances, and less conservative in other cases. In general, the current distances become less conservative than those from Risering Steel Castings as the width-to-thickness ratio W/T of a casting section increases.• Consistent definitions of feeding distance: The definitions of feeding distance for top risers with end effect, top risers with lateral feeding, and side risers are the same in this work, whereas different definitions were used in Risering Steel Castings.• Multipliers for different conditions: Multipliers are provided to tailor feeding distances to different cast alloy compositions, mold materials, pouring superheats, and desired levels of casting soundness.• Soundness in terms of riser zones and end zones: The information presented in Section 4 is intended to give the foundry engineer a physical understa...

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“…It is assumed that thermal properties ρ, ceff and keff are functions of T and of steel components. [10][11][12] Considering the symmetry, one quarter of the billet slice (transverse cross section) is selected to be the calculation domain. Each slice starts at the meniscus (assumed flat) and moves along the strand through the mold, secondary cooling zones (SCZ) and the air cooling zone sequentially.…”

Section: Introductionmentioning

confidence: 99%

Real-time Heat Transfer Model Based on Variable Non-uniform Grid for Dynamic Control of Continuous Casting Billets

Yang

Xie

et al. 2014

ISIJ Int.

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A real-time heat transfer model has been developed for dynamic control of continuous casting billets. In order to fulfill the real-time requirements for efficiency and accuracy, finite volume method (FVM) and alternating direction implicit algorithm (ADI) have been selected as the efficient numerical solution algorithm. And most important of all, variable non-uniform grid and variable time step have been adopted in the algorithm, accelerating the calculation of heat transfer model by 20-40 times. Further, the algorithm's discretization parameters including the grid, time step and slice distance have been optimized by error control (< 3°C), improving the relative calculation time to 0.57. The real-time model has been calibrated by surface temperature measurements using a thermal infrared imager, and its online performance has been tested, within ±13°C of the measurements. After the calibration, the model has been applied to the dynamic control of secondary cooling and the dynamic control of the electric current of final electromagnetic stirring (FEMS), providing the surface temperatures and mushy zone radius at the installation position of FEMS. The latter has been validated by shell-thickness measurements using nail-shooting (relative error< 2.3%). The model-based dynamic control systems controlled the surface temperatures and the flow in mushy zone precisely with changing casting conditions, and have improved the billet quality obviously and reduced the fractures of rolling line during wire-drawing to 1/6 times per month in average from 2-3 times before.

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Calculation of thermophysical properties of carbon and low alloyed steels for modeling of solidification processes

Cited by 54 publications

References 9 publications

Numerical Predictions for the Thermal History, Microstructure and Hardness Distributions at the HAZ during Welding of Low Alloy Steels

Numerical Predictions for the Thermal History, Microstructure and Hardness Distributions at the HAZ during Welding of Low Alloy Steels

Yield Improvement in Steel Casting (Yield II)

Real-time Heat Transfer Model Based on Variable Non-uniform Grid for Dynamic Control of Continuous Casting Billets

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