This research introduces a novel framework integrating stochastic finite element analysis (FEA) with advanced circular statistical methods to optimize heat pump efficiency under material uncertainties. The proposed methodologies and optimization focus on balancing the mean efficiency and variability by adjusting the concentration parameter of the Von Mises distribution, which models directional variability in thermal conductivity. The study highlights the superiority of the Von Mises distribution in achieving more consistent and efficient thermal performance compared to the uniform distribution. We also conducted a sensitivity analysis of the parameters for further insights. The results show that optimal tuning of the concentration parameter can significantly reduce efficiency variability while maintaining a mean efficiency above the desired threshold. This demonstrates the importance of considering both stochastic effects and directional consistency in thermal systems, providing robust and reliable design strategies.

This paper proposes to apply a microfluidic chip combining DSC, DTA, and PCR-like functions for studying synthesis and selection of precursors of the genetic code carriers at hydrothermal conditions including those in natural high frequency fields (such as magnetosphere emission, atmospherics, auroras and lightings).

It is proposed to use angular descriptors (in polar and Euler coordinates or quaternions), as well as radiation patterns of many variables, in HF radiofrequency and microwave thermal analysis of anisotropic systems.

The last decades have offered new challenges to researchers worldwide through the problems our planet is facing both in the environment protection field and the need to replace fossil fuels with new environmentally friendly alternatives. Bioenergy as a form of renewable energy is an acceptable option from all points of view and biofuels due to their biological origin have the ability to satisfy the new needs of humanity. By releasing some non-polluting combustion products into the atmosphere, biofuels have already been adopted as additives in traditional liquid fuels, being intended mainly for internal combustion engines of automobiles. The current work proposes an extension of biofuels application in combustion processes specific to industrial furnaces. This technical concern is not found in the literature, except for achievements of the research team involved in this work, which has performed previous investigations. A 51.5 kW-burner was designed to operate with glycerine originating from triglycerides of plants and animals, mixed with ethanol, an alcohol produced by the chemical industry recently used as an additive in gasoline for automobile engines. Industrial oxygen was chosen as the oxidizing agent necessary for the liquid mixture combustion, allowing to obtain much higher flame temperatures compared to the usual combustion processes using air. Mixing glycerine with ethanol in 8.8 ratio allowed growing flame stability, accentuated also by creating swirl currents in the flame through the speed regime of fluids at the exit from the burner body. Results were excellent both through the flame stability and low level of polluting emissions.

With the wide application of the Internet and smart systems, data centers (DCs) have become a hot spot of global concern. The energy saving for data centers is at the core of the related works. The thermal performance of a data center directly affects its total energy consumption, as cooling consumption accounts for nearly 50% of total energy consumption. Superior power distribution is a reliable method to improve the thermal performance of DCs. Therefore, analyzing the effects of different power distribution on thermal performance is a challenge for DCs. This paper analyzes the thermal performance numerically and experimentally in DCs with different power distribution. First, it uses Fluent simulate the temperature distribution and flow field distribution in the room, taking the cloud computing room as the research object. Then, it summarizes a formula based on the computing power distribution in a certain range by the numerical and experimental analysis. Finally, it calculates an optimal cooling power by analyzing the cooling power distribution. The results shows that it reduces the maximum temperature difference between the highest temperature of the cabinet from 5-7k to within 1.2k. In addition, the cooling energy consumption is reduced by more than 5%.