Optimal Design of EMALS Based on a Double-Sided Tubular Linear Induction Motor

Musolino, Antonino; Raugi, Marco; Rizzo, Rocco; Tucci, Mauro

doi:10.1109/tps.2015.2413675

Cited by 26 publications

(10 citation statements)

References 17 publications

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“…A commonly used monitoring method in recent years, due to its cost-effectiveness and rapid diagnosis, is the thermographic non-destructive test (TNDT), which is conducted by infrared thermography [9]. This technology allows different applications of pre-diagnosis and diagnosis of abnormal working conditions such as innovative electrical machines (Electromagnetic Aircraft Launch System-EMALS) [10], electric equipment [11] and in particular in the field of PV systems, because it allows us to identify the operating status of photovoltaic modules and other components (cables, junction boxes, etc.) without interrupting normal systems operation [12].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Dust and Hotspot Classification in PV Modules

et al. 2020

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This paper proposes an innovative approach to classify the losses related to photovoltaic (PV) systems, through the use of thermographic non-destructive tests (TNDTs) supported by artificial intelligence techniques. Low electricity production in PV systems can be caused by an efficiency decrease in PV modules due to abnormal operating conditions such as failures or malfunctions. The most common performance decreases are due to the presence of dirt on the surface of the module, the impact of which depends on many parameters and conditions, and can be identified through the use of the TNDTs. The proposed approach allows one to automatically classify the thermographic images from the convolutional neural network (CNN) of the system, achieving an accuracy of 98% in tests that last a couple of minutes. This approach, compared to approaches in literature, offers numerous advantages, including speed of execution, speed of diagnosis, reduced costs, reduction in electricity production losses.

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Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Dust and Hotspot Classification in PV Modules

et al. 2020

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“…Area of their use is constantly expanding: drives of manipulators and industrial robots; in mining, electric power and other industries. Researchers pay a lot of attention to problem of optimization of TLIM, perfection of their characteristics [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Mathematical Model of Tubular Linear Induction Motor

Okhrimenko¹,

Zbіtnieva²

2021

MMEP

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Problem of calculation of distribution of magnetic field induction in clearance of tubular linear induction motor (TLIM) is considered. Mathematical model is represented by Fredholm integral equations of second kind for complexes of electric field strength and density of coupled magnetization currents at interface of environments. Algorithm of calculation of distribution of magnetic field induction in TLIM clearance has been developed. Dependence of magnetic field induction in motor clearance on value of pole division is investigated. There is area of optimum pole pitch. Reliability of results of calculations on mathematical model is confirmed by their comparison with results obtained on physical model. Calculated dependence of induction on thickness of runner's iron circuit also has extreme character. Given model can be used at design stage of TLIM. Model allows calculating its optimal geometric dimensions based on criterion of maximum induction in motor clearance, taking into account physical properties of applied materials.

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“…The Electro-Magnetic Aircraft Launch System (EMALS) is another important example of application of the electromagnetic launch technology. It has been introduced in substitution of the steam catapults for the take-off of airplanes from the new class of carriers of the US Navy [8][9][10][11]. With respect to the steam catapult EMALS is able to produce a smooth and controllable acceleration profile with a consequent reduction of the stress on the aircrafts.…”

Section: Introductionmentioning

confidence: 99%

Numerical 3D Simulation of a Full System Air Core Compulsator-Electromagnetic Rail Launcher

et al. 2020

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Multiphysics problems represent an open issue in numerical modeling. Electromagnetic launchers represent typical examples that require a strongly coupled magnetoquasistatic and mechanical approach. This is mainly due to the high velocities which make comparable the electrical and the mechanical response times. The analysis of interacting devices (e.g., a rail launcher and its feeding generator) adds further complexity, since in this context the substitution of one device with an electric circuit does not guarantee the accuracy of the analysis. A simultaneous full 3D electromechanical analysis of the interacting devices is often required. In this paper a numerical 3D analysis of a full launch system, composed by an air-core compulsator which feeds an electromagnetic rail launcher, is presented. The analysis has been performed by using a dedicated, in-house developed research code, named “EN4EM” (Equivalent Network for Electromagnetic Modeling). This code is able to take into account all the relevant electromechanical quantities and phenomena (i.e., eddy currents, velocity skin effect, sliding contacts) in both the devices. A weakly coupled analysis, based on the use of a zero-dimensional model of the launcher (i.e., a single loop electrical equivalent circuit), has been also performed. Its results, compared with those by the simultaneous 3D analysis of interacting devices, show an over-estimate of about 10–15% of the muzzle speed of the armature.

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Optimal Design of EMALS Based on a Double-Sided Tubular Linear Induction Motor

Cited by 26 publications

References 17 publications

Convolutional Neural Network for Dust and Hotspot Classification in PV Modules

Convolutional Neural Network for Dust and Hotspot Classification in PV Modules

Mathematical Model of Tubular Linear Induction Motor

Numerical 3D Simulation of a Full System Air Core Compulsator-Electromagnetic Rail Launcher

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