In the present study, the flame response and the effect of equivalence ratio and inlet temperature on the flame response are numerically investigated using Weller flamelet combustion and LES turbulent models. The results show that with increasing excitation amplitude at a constant frequency, theheat release ratio increases; the increment is smaller at higher frequencies. Due to the combustor geometry, two recirculating zones are formed. Any change in the amplitude and frequency can affect these recirculation zones, especially the central recirculation zone. At the low frequencies (below 50Hz), increasing the excitation amplitude affects flame transfer functioninconsiderably, because of no influence of the recirculation zoneson the heat release. At higher frequencies, an increase in the amplitude has a more influence on value of flame transfer function. It is shown that by increasing the amplitude, up to frequency of 140 Hz, the phase of flame transfer function slightly reduced, while this is intensified with increasing the equivalence ratio or flame inlet temperature. Furthermore, by increasing the equivalence ratio or inlet temperature of the flame, heat release ratio and the flame transfer function are reduced.

Thermal radiation plays a key role in the heat transfer between the flame and its surroundings. It is essential to provide a reliable method for measurement of flame radiation in the combustion study. Also, it is challenging to measure the radiation flux from the flame in the chamber due to the effect of the walls. The radiation emitted from the walls and the reflection of the flame radiation from the walls interferes with the measurement of the flame radiation. High temperature or high reflection walls can increase the error in the measurement of flame radiation. In this paper, various parameters affecting the flame radiation have been investigated. These studies are based on the wall incident radiation and the wall radiation heat flux. To calculate the flame radiation, a theoretical method is presented which is compared with the CFD simulation results to confirm its correctness. To simulate the flame SM1 of the University of Sydney, a steady flamelet combustion model has been used with the k-ε modified turbulence model. Due to the low optical thickness of the model, the DO radiation model is used to simulate CFD. The CFD results are in good agreement with theoretical results, and the estimation of flame emission are accurately acceptable. The results show that the flame radiation differs from the wall radiation by more than 25%, when the wall radiation coefficient will be smaller than 0. 8 or the wall temperature will be more than 330k.

Compared to liquid and solid fuels, natural gas produces much less soot during combustion, and the melting and reaction on the surface of these soot is one of the main factors in the production of radiation in the flame. The use of natural gas as a substitute for liquid fuel in industrial and household burners has caused a decrease in thermal efficiency due to the weak radiation bands of gas fuel, therefore the combustion of natural gas has a lower thermal efficiency compared to liquid and solid fuels. And improving the radiant heat transfer of natural gas flame is one of the main challenges of active researchers in this field. In this research, the ethanol fuel was reduced to micron size by the nebulizer and injected into the flame with turbulent flow and as a result, better mixing, and its effects on the radiant heat transfer of the flame as well as the reduction of NOX pollutant were investigated. The results of the present research show that the injection of 1 percent by weight of ethanol in a nebulized form causes a 1 percent increase in the average temperature of the natural gas flame along the length of the boiler and a 3.5 percent increase in the radiant heat transfer flux, and also the injection of micron ethanol particles causes the production of NOX pollutants to decrease by 12.5 percent. 

Face recognition from digital images is used for surveillance and authentication in cities, organizations, and personal devices. Internet of Things (IoT)-powered face recognition systems use multiple sensors and one or more servers to process data. All sensor data from initial methods was sent to the central server for processing, raising concerns about sensitive data disclosure. The main concern was that all data from all sectors that could contain confidential information was placed in a central server. Federated learning can solve this problem by using several local model training servers for each region and a central aggregation server to form a global model in IoT networks. This article presents a novel approach to optimize data transfer and convergence time in federated learning for a face recognition task using Non-dominated Sorting Genetic Algorithm II (NSGA II). The aim of the study is to balance the trade-off between training time and model accuracy in a federated learning environment. The results demonstrate the effectiveness of the proposed approach in reducing data transfer and convergence time, leading to improved performance in face recognition accuracy. This research provides insights for researchers and practitioners to enhance the efficiency of federated learning in real-world applications.