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The interruption event will seriously affect the normal operation of urban rail transit lines,causing a large number of passengers to be stranded in the station and even making the train stranded in the interval between stations. This study aims to reduce the impact of interrupt events and improve service levels.

To address this issue, this paper considers the constraints of train operation safety, capacity and dynamic passenger flow demand. It proposes a method for adjusting small loops during interruption events and constructs a train operation adjustment model with the objective of minimizing the total passenger waiting time. This model enables the rapid development of train operation plans in interruption scenarios, coordinating train scheduling and line resources to minimize passenger travel time and mitigate the impact of interruptions. Regarding the proposed train operation adjustment model, an improved genetic algorithm (GA) is designed to solve it.

The model and algorithm are applied to a case study of interruption events on Beijing Subway Line 5. The results indicate that after solving the constructed model, the train departure intervals can be maintained between 1.5 min and 3 min. This ensures both the safety of train operations on the line and a good match with passengers’ travel demands, effectively reducing the total passenger waiting time and improving the service level of the urban rail transit system during interruptions. Compared to the GA algorithm, the algorithm proposed in this paper demonstrates faster convergence speed and better computational results.

This study explicitly outlines the adjustment method of using short-turn operation during operational interruptions, with train departure times and station stop times as decision variables. It takes into full consideration safety constraints on train operations, train capacity constraints and dynamic passenger demand. It has constructed a train schedule optimization model with the goal of minimizing the total waiting time for all passengers in the system.

Numerical magnetic field analysis is used for predicting the performance of an induction motor and a slip‐ring generator having different faults implemented in their structure. Virtual measurement data provided by the numerical magnetic field analysis are analysed using modern signal processing techniques to get a reliable indication of the fault. Support vector machine based classification is applied to fault diagnostics. The stator line current, circulating currents between parallel stator branches and forces between the stator and rotor are compared as media of fault detection.

To make wind energy (one of the alternative-energy production technologies) economical, wind-turbines (convertors of wind energy into electrical energy) are required to operate, with only regular maintenance, for at least 20 years. However, some key wind-turbine components (especially the gear-box) often require significant repair or replacement after only three to five years in service. This causes an increase in both the wind-energy cost and the cost of ownership of the wind turbine. The paper aims to discuss these issues.

To overcome this problem, root causes of the gear-box premature failure are currently being investigated using mainly laboratory and field-test experimental approaches. As demonstrated in many industrial sectors (e.g. automotive, aerospace, etc.) advanced computational engineering methods and tools cannot only complement these experimental approaches but also provide additional insight into the problem at hand (and do so with a substantially shorter turn-around time). The present work demonstrates the use of a multi-length-scale computational approach which couples large-scale wind/rotor interactions with a gear-box dynamic response, enabling accurate determination of kinematics and kinetics within the gear-box bearings (the components most often responsible for the gear-box premature failure) and ultimately the structural response (including damage and failure) of the roller-bearing components (e.g. inner raceways).

It has been demonstrated that through the application of this approach, one can predict the expected life of the most failure-prone horizontal axis wind turbine gear-box bearing elements.

To the authors’ knowledge, the present work is the first multi-length-scale study of bearing failure in wind-turbines.

As the techniques of computer chip manufacturing are applied to etching the surface of lenses in extremely precise ways, a world of optical marvels unknown to traditional lens‐grinders will come swimming into view.

The compressible Navier‐Stokes equations are solved numerically for turbulent transonic aerospace applications on parallel computers. An Explicit Algebraic Reynolds Stress Model (EARSM) models the turbulence. Expressing the EARSM as an extension of an eddy‐viscosity model makes the implementation straightforward in a flow solver with existing two‐equation eddy‐viscosity models. The k−ω transport equations are used as a platform for the model. The EARSM approach significantly improves the shock position for transonic flow over wings without substantial increase in computational cost. Industrial use of advanced flow modelling requires a short turn‐around time of computations. This is enabled through the use of parallel computers. To achieve good parallel performance the computational load has to be evenly distributed between the processors of the parallel computer. A heuristic algorithm is described for distributing and splitting the blocks of a structured multiblock grid for a good static load balance. Speed‐up results are presented for turbulent flow around a wing on a number of parallel platforms.

The aim of this paper is to present the latest advances in real‐time airborne hyperspectral sensing applications in identifying and mapping the likely spots to be zeroed in for the SAR operations.

A Sabah Air GAF Nomad N22B low altitude fixed wing aircraft equipped with an AISA airborne hyperspectral imaging system flew over the steep gradient carved by very narrow valleys, ridges, precipitous escarpments and ravines, extensively covered by thick virgin forest of the highlands. The study was carried out by an AISA sensor, which is a complete system that consists of a compact hyperspectral sensor head, miniature GPS/INS sensor for precise positioning, data acquisition unit and Caligeo post‐processing software.

These UPM‐APSB's AISA flights demonstrated that quality real‐time hyperspectral images could be pre‐processed on‐board the aircraft and become accessible to the SAR committee members for quick ground SAR within two hours after the flight. The combined geospatial information technologies were a major breakthrough in the Malaysian SAR real‐time imaging technique.

It is expected that future precise locations of the suspected targets can be transmitted via very high frequency radio communications and become accessible to the SAR ground members such as the commandos VAT 69. Real‐time airborne hyperspectral imaging will benefit strategic SAR and help reduce the loss of lives in future helicopter crashes in the Bario‐Ba'Kelalan areas.

The purpose of this paper is to describe a technique for modeling transformer internal faults using transmission line modeling (TLM) method. In this technique, a model for simulating a two winding single phase transformer is modified to be suitable for simulating an internal fault in both windings.

TLM technique is mainly used for modeling transformer internal faults. This was first developed in early 1970s for modeling two‐dimensional field problems. Since, then, it has been extended to cover three dimensional problems and circuit simulations. This technique helps to solve integro‐differential equations of the analyzed circuit. TLM simulations of a single phase transformer are compared to a custom built transformer in laboratory environment.

It has been concluded from the real time studies that if an internal fault occurs on the primary or secondary winding, the primary current will increase a bit and secondary current does not change much. However, a very big circulating current flows in the shorted turns. This phenomenon requires a detailed modeling aspect in TLM simulations. Therefore, a detailed inductance calculation including leakages is included in the simulations. This is a very important point in testing and evaluating protective relays. Since, the remnant flux in the transformer core is unknown at the beginning of the TLM simulation, all TLM initial conditions are accepted as zero.

The modeling technique presented in this paper is based on a low frequency (up to a few kHz) model of the custom‐built transformer. A detailed capacitance model must be added to obtain a high‐frequency model of the transformer. A detailed arc model, aging problem of the windings will be applied to model with TLM + finite element method.

Using TLM technique for dynamical modeling of transformer internal faults is the main contribution. This is an extended version of an earlier referenced paper of the authors and includes inductance calculation, leakages calculation, and BH curve simulation while the referenced paper only includes piecewise linear inductance values. This modeling approach may help power engineers and power system experts understand the behavior of the transformer under internal faults.