How does machine learning helps in heat transfer applications?5 answersMachine learning (ML) plays a crucial role in enhancing heat transfer applications by providing efficient analysis and prediction capabilities. ML techniques are utilized to analyze vast amounts of data collected from experiments, field observations, and simulations in the heat transfer field. These techniques offer a more computationally efficient approach compared to traditional methods like Finite Element Analysis (FEA) for thermal response prediction in structural fire engineering applications. Moreover, ML algorithms are employed to develop data-driven surrogate models for predicting the two-phase heat transfer coefficient (HTC) in compact heat exchangers, outperforming traditional empirical correlations. ML also aids in building reduced-order models, optimizing thermal systems, and predicting heat transfer coefficients in various heat transfer applications, showcasing its versatility and effectiveness in this domain.
How is the heat transfer coefficient experimentally modelled or determined?5 answersThe heat transfer coefficient is determined experimentally using various methods. One common approach is to measure the temperature distribution on the surface of the object and use it to calculate the heat transfer coefficient. This can be done by placing thermocouples on the surface and measuring the temperatures. Another method involves heating the object to a known temperature and then inducing a thermal transient by injecting ambient air into its internal cooling system. The external surface temperature is recorded using an infrared camera, and this data is used in a numerical procedure to update the heat flux on the internal surface and calculate the convective heat transfer coefficient. In some cases, the heat transfer coefficient cannot be directly measured, so inverse methods are used. These methods involve assuming an unknown boundary condition and using additional information from sensors located at a certain distance from the surface to calculate the heat transfer coefficient. Additionally, correlations for the heat transfer coefficient can be determined by simultaneously solving for the coefficients in Nusselt number correlations using experimental data.
What is the formula for specific heat?4 answersThe formula for specific heat is given by the equation C = lim(dQ/dT), where C is the specific heat, dQ is the heat input, and dT is the change in temperature. Another formula for specific heat is derived for the non-equilibrium steady state, where it can be calculated by analyzing fluctuations in the steady state or by relating it to the transient response observed when an ensemble of equilibrium systems is perturbed. In the context of high pressure measurements, the specific heat is obtained using the formula cp = C(T,P)U I / m ė, where cp is the specific heat, C(T,P) is a function determined experimentally, U is the voltage drop, I is the current, m is the mass of the sample, and ė is the time derivative of the bridge output voltage. For a free particle in a cubic box with reflecting walls, the specific heat is a function of variable η and approaches different values depending on the value of η. In the study of a one-dimensional phonon system, the specific heat is calculated using the path integral method applied to the expression for the partition function.
How fat affect beef thermal conductivity during cooling?3 answersFat content has a significant effect on beef thermal conductivity during cooling. The abstracts indicate that the thermal conductivity of beef decreases with increasing fat content. In the study by Baghe-Khandan et al., it was found that there is a correlation between beef thermal conductivity and fat content. Additionally, the abstract by Sun and McDonald states that vacuum cooled beef samples, which had higher moisture loss, had lower thermal conductivity compared to other cooling methods. Therefore, it can be concluded that higher fat content in beef leads to lower thermal conductivity during cooling.
What is the heat transfer coefficient of li-po battery?5 answersThe heat transfer coefficient of a lithium polymer (Li-po) battery can be determined using various methods. One approach is to use an entropic coefficient based on the inverse heat transfer problem. Another method involves using nanofluids, such as tetrahydrofuran-graphene nanofluid, to improve the thermal conductivity and reduce thermal resistance. Additionally, the use of phase change materials (PCMs), such as expanded graphite PCM, can enhance the heat transfer rate in Li-po batteries. Experimental data and simulations have shown that the temperature and heat produced by Li-ion batteries increase with higher C-rates, with the highest temperatures observed near the tabs and in the internal space of the battery. However, the specific heat transfer coefficient of a Li-po battery is not explicitly mentioned in the provided abstracts.
How does the heat transfer coefficient affect the thermal performance ratio?5 answersThe heat transfer coefficient has a significant impact on the thermal performance ratio. Experimental research conducted by Sundar et al.showed that using hybrid nanofluids with higher heat transfer coefficients in a heat pipe resulted in lower wall temperatures at the evaporator and condenser sections, leading to improved thermal performance. Additionally, the study found that the heat transfer coefficients of the evaporator and condenser increased when using hybrid nanoparticles based nanorefrigerants compared to the base fluid. Another study by Houanalyzed the effects of heat transfer on the net work output and indicated thermal efficiency of an air standard Dual cycle. The results showed that higher heat transfer to the combustion chamber walls reduced the peak temperature and pressure, resulting in lower work per cycle and efficiency. Therefore, a higher heat transfer coefficient generally leads to improved thermal performance, while a lower heat transfer coefficient can negatively impact the performance.