In this paper, the speed control of the real DC motor is experimentally investigated using nonlinear PID neural network controller. As a simple and fast tuning algorithm, two optimization techniques are used; trial and error method and particle swarm optimization PSO algorithm in order to tune the nonlinear PID neural controller's parameters and to find best speed response of the DC motor. To save time in the real system, a Matlab simulation package is used to carry out these algorithms to tune and find the best values of the nonlinear PID parameters. Then these parameters are used in the designed real time nonlinear PID controller system based on LabVIEW package. Simulation and experimental results are compared with each other and showe

Flexure members such as reinforced concrete (RC) simply supported beams subjected to two-point loading were analyzed numerically. The Extended Finite Element Method (XFEM) was employed for the treatment the non-smooth h behaviour such as discontinuities and singularities. This method is a powerful technique used for the analysis of the fracture process and crack propagation in concrete. Concrete is a heterogeneous material that consists of coarse aggregate, cement mortar and air voids distributed in the cement paste. Numerical modeling of concrete comprises a two-scale model, using mesoscale and macroscale numerical models. The effectiveness and validity of the Meso-Scale Approach (MSA) in modeling of the reinforced concrete beams w

The analysis of rigid pavements is a complex mission for many reasons. First, the loading conditions include the repetition of parts of the applied loads (cyclic loads), which produce fatigue in the pavement materials. Additionally, the climatic conditions reveal an important role in the performance of the pavement since the expansion or contraction induced by temperature differences may significantly change the supporting conditions of the pavement. There is an extra difficulty because the pavement structure is made of completely different materials, such as concrete, steel, and soil, with problems related to their interfaces like contact or friction. Because of the problem's difficulty, the finite element simulation is

Four simply supported reinforced concrete (RC) beams were test experimentaly and analyzed using the extended finite element method (XFEM). This method is used to treat the discontinuities resulting from the fracture process and crack propagation in that occur in concrete. The Meso-Scale Approach (MSA) used to model concrete as a heterogenous material consists of a three-phasic material (coarse aggregate, mortar, and air voids in the cement paste). The coarse aggregate that was used in the casting of these beams rounded and crashed aggregate shape with maximum size of 20 mm. The compressive strength used in these beams is equal to 17 MPa and 34 MPa, respectively. These RC beams are designed to fail due to flexure when subjected to lo

The current paper aims to investigate the stress distribution developed in Kennedy Class II mandibular distal extension removable partial dentures due to applying a unilateral load condition in both vertical and lateral oblique directions. 3D models of mandible bone and RPD framework were first built based on actual patient data and later exported to ANSYS software to implement the numerical analysis. For realistic analysis, the model considered the frictional contact between the RPD retainers with the teeth and mucosa with the resin denture base by applying the feature of small sliding. To ensure maximum longevity and suitability of restoration, two different metallic RPDs constructed from commercially pure titanium (CP Ti) and cob

Single-photon detection concept is the most crucial factor that determines the performance of quantum key distribution (QKD) systems. In this paper, a simulator with time domain visualizers and configurable parameters using continuous time simulation approach is presented for modeling and investigating the performance of single-photon detectors operating in Gieger mode at the wavelength of 830 nm. The widely used C30921S silicon avalanche photodiode was modeled in terms of avalanche pulse, the effect of experiment conditions such as excess voltage, temperature and average photon number on the photon detection efficiency, dark count rate and afterpulse probability. This work shows a general repeatable modeling process for significant perform

Metasurface polarizers are essential optical components in modern integrated optics and play a vital role in many optical applications including Quantum Key Distribution systems in quantum cryptography. However, inverse design of metasurface polarizers with high efficiency depends on the proper prediction of structural dimensions based on required optical response. Deep learning neural networks can efficiently help in the inverse design process, minimizing both time and simulation resources requirements, while better results can be achieved compared to traditional optimization methods. Hereby, utilizing the COMSOL Multiphysics Surrogate model and deep neural networks to design a metasurface grating structure with high extinction rat