The use of gas energy includes a wide range of applications to directly accelerate the liquid in a pipeline without the aid of mechanical equipment, such as marine gas-liquid jet propulsion. To clarify the characteristics of energy transfer by interphase forces for gas-liquid flows in variable cross-section tubes, two-fluid models of annular flow, bubbly flow and homogeneous flow were adopted, respectively, along with four newly elaborated coefficients, which are the work factor of gas $f_g$, reflecting the relative ability of gas to power liquid, the interface work transfer coefficient $k_g$ (representing the relative magnitude of mechanical work received by liquid from gas), the interphase work-to-energy conversion coefficient $k_l$ (denoting the capability of energy transfer through work performed by interphase forces) and the interphase mechanical efficiency ${\eta }_w$. The results reveal the interphase work transfer is strongly influenced by the structural parameters of the tubes (or nozzles), and an optimized design is necessary to improve the performance. The higher the degree of gas dispersion in the liquid, the more advantageous the conversion of gas work into the liquid’s mechanical energy. Of these three flow patterns, annular flow has the lowest $k_l$ and ${\eta }_w$ ($k_l$ = 0.0797, ${\eta }_w$ = 0.9885 in present example), while homogeneous flow displays the limit of interphase mechanical energy conversion because the gas-liquid momentum coupling reaches the maximum ($k_l$ = 0.9979, ${\eta }_w$ = 1).

Nowadays, due to stricter pollution standards, more attention has been focused on pollutants emitted from cars. As a very dangerous pollutant, NO_x has always triggered the sensitivity of the related organizations. In the process of developing and designing the engine, estimating the amount of this pollutant is of great importance to reduce future expenses. Calculating the amount of this pollutant has usually been complicated and prone to error. In the present paper, neural networks have been used to find the coefficients of correcting NO_x calculation. The Zeldovich method calculated the value of NO_x with 20% error. By applying the progressive neural network and correcting the equation coefficient, this value decreased. The related model has been validated with other fuel equivalence ratios. The neural network model has fitted the experimental points with a convergence ratio of 0.99 and a squared error of 0.0019. Finally, the value of NO_x anticipated by the neural network has been calculated and validated according to empirical data by applying maximum genetic algorithm. The maximum point for the fuel composed of 20% hydrogen and 80% methane occurred in the equivalence ratio of 0.9; and the maximum point for the fuel composed of 40% hydrogen occurred in equivalence ratio of 0.92. The consistency of the model findings with the empirical data shows the potential of the neural network in anticipating the amount of NO_x.

Recently, energy costs have increased significantly, and energy savings have become more important, leading to the use of different patterns to align with the characteristics of demand-side load. This paper focused on the energy management of low-rise university buildings, examining the demand response related to air conditioning and lighting by measuring the main parameters and characteristics and collecting and managing the data from these parameters and characteristics. This system seeks to control and communicate with the aim of reducing the amount of peak energy using a digital power meter installed inside the main distribution unit, with an RS-485 communication port connected to a data converter and then displayed on a computer screen. The demand response and time response were managed by power management software and an optimization model control algorithm based on using a split type of air conditioning unit. This unit had the highest energy consumption in the building as it works to provide a comfortable environment based on the temperatures inside and outside the building. There was a renewable energy source that compensated for energy usage to decrease the peak load curve when the demand was highest, mostly during business hours. An external power source providing 20 kWh of solar power was connected to an inverter and feeds power into each phase of the main distribution. This was controlled by an energy power management program using a demand response algorithm. After applying real-time intelligent control demand-side management, the efficient system presented in this research could generate energy savings of 25% based on AC control of the lighting system. A comparison of the key system parameters shows the decrease in power energy due to the use of renewable energy and the room temperature control using a combination of split-type air conditioning.