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strength of heating element.
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One of such properties is the thermal conductivity that characterizes the strength of heat conduction and has become a research focus of nanofluid society in the last decade [1 9].
In this study, an artificial neural network (ANN) model was developed for predicting an optimum bonding strength of heat treated woods.
A probabilistic model was developed to predict fatigue strength of heat treated high carbon steel and compared with the experimental data.
The thermal interactions with ground and the GCHP performance are strictly related to a number of factors, including the ground properties, the BHE field geometry, the sequence and strength of heat loads required by the building.
This paper presents results of an experimental study undertaken to optimize the residual compressive strength of heated high performance concrete using the Taguchi off-line method and the utility concept.
FA improved the residual compressive strength of heated RAC the most followed by MK, PSA, cement, and SF although SF, MK, and PSA concrete showed the higher improvement of compressive strength than others at 20 °C.
The strength of the heat-treated composites was significantly enhanced through heat treatments, when compared with the composites with one-scale architecture and Ti6Al4V alloy.
A recursive algorithm is developed to solve the inverse heat conduction problem of estimating time-varying strength of a heat source from the knowledge of temperature readings taken inside the domain.
This results in lower values of the compressive strength of the heat-treated concrete.
Thus, the dispersant still affected the strength of the heated samples.
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