— blue light-emitting-diodes (LEDs) are special and different then the other LEDs due to their high-efficient lighting. They have large badgap energy. So gallium nitrides are mostly used during designing blue light. This application focus on the emission properties of a InGaN LED. The emission intensity, energy diagram, spectrum, and efficiency are calculated for an applied voltage. The indium and GaN composition in the blue LED can be varied to control the emission wavelength. In here, composition of InGaN and operational voltage values were varied to control wavelength. Effects of the different InGaN composition and different voltage values were given in results as comperehansively for 5 different simulations. This study is dissimalar than other wevelength studies due to used original parameter values and wavelength compared methods for many situations about InGaN percentage and volatage values. As a result of simulations, we can infer that the high In_x percentage in composition and high voltage makes wavelength gap of an blue LED larger.

Advances in semiconductor technologies, particularly in power transistors and switching diodes, have enabled higher switching frequencies and converter efficiency, renewing interest in Switched Reluctance Motors (SRMs) for electric vehicles. This work presents a data-driven approach utilizing a Long Short-Term Memory (LSTM) network capable of effectively managing temporal dependencies for estimating rotor position without sensors in SRMs. The motor investigated was custom-designed, subsequently manufactured as a prototype. The LSTM was trained and validated with experimental data collected at various speeds and load conditions. The outcomes demonstrate the model’s strong performance, with a mean squared error (MSE) of ${1.77}^{{°}^2}$, a mean absolute error (MAE) of 1.09°, and 97.35% accuracy. Compared to typical estimation methods such as back-electromotive force (EMF)-based techniques, fuzzy logic, model predictive control, feed-forward neural networks (FFNNs), and back-propagation neural networks (BPNNs), the LSTM stands out as one of the most effective and widely used models. Previous neural networks (NN)-based studies typically report ±5° accuracy, whereas LSTM keeps the error about 1° in this study. This strategy eliminates position sensors, reduces cost and complexity, and enables reliable real-time SRM control. Results indicate that the method has significant potential for electric motor drives, particularly for SRMs.

The Switched Reluctance Motor (SRM) is one of the oldest types of electric motors. SRMs have high torque fluctuations as they have a salient pole structure. The popularity of Switched Reluctance Motors has increased in recent years due to their simple structure, ruggedness, reliability, as well as being inexpensive to manufacture and having a high torque to mass ratio. However, there are some disadvantages, such as high torque ripple, noise and the need for an advanced control system. So they are not extensively used in industry. This study focused on the torque ripple and the speed control aspects. Here, the two most popular and similar Switched Reluctance Motors with regard to motor size, called the 6/4 and 8/6 SRM, have been compared according to the dynamic behavior of their motor parameters. Also, the converters, loads and given power values are the same for the two motor systems. The motor parameters were controlled via MatLab Simulink software. Although the 6/4 and 8/6 models are identical and have the same power converters systems and position sensors, they show different motor behavior due to their dissimilar magnetic structures.

This study presents a comparison of Double Stator Single Rotor Axial Flux Inner Rotor North–South (DSSR AFIR NS) and Double Stator Single Rotor Axial Flux Inner Rotor North–North (DSSR AFIR NN) configurations based on the magnetic equivalent circuit (MEC) approach. In the first stage, a magnetic equivalent circuit model of the DSSR AFIR NS topology was developed, and the mathematical formulations of the air-gap reluctance, stator tooth reluctance, stator yoke reluctance, and rotor core reluctance constituting the magnetic flux path were derived. Considering the motor’s geometric and electromagnetic characteristics, the magnetic flux and flux density distributions in each region were analytically evaluated. The obtained results were then validated through finite element analysis (FEA). In the second stage, the DSSR AFIR NN topology was investigated using the same methodology, and the electromagnetic performances of the two machines with identical slot numbers, pole numbers, and physical dimensions were compared to determine their respective advantages and limitations. Numerical analyses show the AFIR-NN topology yields higher torque (26.53 Nm) than AFIR-NS (19.43 Nm). Conversely, AFIR-NS exhibits superior magnetic characteristics, with higher back-EMF (34.73 V vs. 19.37 V) and air-gap flux density (0.89 T vs. 0.46 T).