Analytical/Finite-element modeling and experimental verification of spray-cooling process in steel

Abstract

An atomizer is a helpful tool that can be used to tailor the cooling rate of steel from the processing temperature in order to get desired properties. It is important to determine the temperature distribution in a specimen subjected to cooling by an atomized spray. A finite-element model for transient heat transfer and thermal-stress analysis is developed to determine the temperature and thermal-stress distribution. The results of the finite-element heat-transfer model are compared with a finite-difference model. The heat-transfer model describes the heat-transfer processes in an AISI 4140 steel cylinder subjected to controlled atomized spray cooling from an initial temperature of 1273 K. The temperature fields predicted by the model are used both to predict the resulting microstructure using continuous cooling transformation (CCT) diagrams and as an input for the thermal-stress model to predict the occurrence of quench cracks. The thermal-stress model incorporates temperature-dependent material properties, heat generation due to phase changes, elastoplastic behavior of steel, and the volumetric expansion associated with the formation of martensite. The results of the finite-element model are verified experimentally by recording temperature profiles, obtaining micrographs, and recording the occurrence of quench cracks.

Anodic plasma electrolytic deposition of composite coating on ferrous alloys with low thermal conductivity and high adhesion strength

“…where t s and t c are the thickness of substrate and coating, respectively; K s the thermal conductivity of a given substrate, which was pre-measured, using the same method above, as 44 W/m•K and 46 W/m•K for AISI 4140 alloy steel and compacted graphite iron, respectively. These values are in Journal Pre-proof J o u r n a l P r e -p r o o f good agreement with reported data [32,33]. It is worth pointing out that the uncertainty of the system was estimated to be 9%, which mainly arose from possible cross-sectional area mismatching or misalignment between the stainless steel bars and test sample, non-uniformity of coating thickness, reliability of temperature measurement, and non-uniform heat flux.…”

A novel analytical–artificial neural network model to improve efficiency of high pressure descaling nozzles in hot strip rolling of steels

“…Experimental investigations showed the following expression 12 t~F tot |N 2I(Hz25) tan (a=2)|0 : 6127 (5) where t is the spray depth, F tot is the total spray force, I …”

“…Spray depth (thickness) of a descaling nozzle depends on spray pressure, flowrate, vertical height, inclination angle and spray angle. Experimental investigations showed the following expression 12 t~F tot |N 2I(Hz25) tan (a=2)|0 : 6127 (5) where t is the spray depth, F tot is the total spray force, I is the spray impact, H is the vertical spray height, a is a calculated spray angle (depending on the spray pressure and flowrate) and N is a constant depending on the nominal spray angle and inclination angle. To find out the values of a and N, the following procedure was introduced.…”

“…3 Many process models have so far been presented for water spray cooling of hot rolled steel bars. 4,5 Nevertheless, less systematic work has been published on modelling of HP hydraulic descaling using flat spray nozzles. Previous studies have been performed to determine the mechanisms of scale removal.…”

Artificial Neural Network Modeling of High Pressure Descaling Operation in Hot Strip Rolling of Steels

“…4,5) Nevertheless, less systematic work has been published on modeling of HP hydraulic descaling using flat spray nozzles. Previous studies have been done to determine the mechanisms of scale removal.…”