Development of a Consequent Pole Transverse Flux Permanent Magnet Linear Machine with Passive Secondary Structure

Transverse-flux machines have the advantage of high force density owing to the peculiarity of decoupling of electric loading and magnetic loading.In this paper,a novel consequent-pole transverse-flux permanent magnet linear machine(CP-TFPMLM)is proposed and investigated.The origination of the proposed machine is from an existing transverse-flux flux-reversal linear machine(TF-FRLM),by partially replacing permanent magnet poles with soft magnetic iron for further reducing the cost of magnets.The fundamental structure and operating principle are introduced at first.The electromagnetic performance,including back EMF,detent force,and thrust force,are investigated with the finite element method.The proposed machine can achieve similar performance as compared to the TF-FRLM but with half of the magnets are used.

Recent Development of Linear Machine Topologies and Applications

Linear machines(LMs)produce linear motion without any intermediate transmission mechanisms,thus the whole electromechanical system has simple structure and its efficiency is high.Because of such merits,linear machines have been studied for a long time and rapidly developed in recent years.Due to the characteristic of open structure,linear machines have more diversity than rotary machines in terms of machine topologies.Accounting for the wide applications of linear machines,e.g.Maglev train,precision machine tools,semiconductor processing device,automatic equipment,logistic transport line,ropeless lifter,compressor,etc.,this paper reviews the most applied linear machines including machine topologies,operating principle and features.In addition,the influence of end effects and the corresponding reduction methods are also summarized.Finally,several commercial applications are exemplified.

Electromagnetic-thermal Coupled Analyses and Joint Optimisation of Electrically-excited Flux-switching Linear Machines

Electrically-excited flux-switching machines are advantageous in simple and reliable structure,good speed control performance,low cost,etc.,so they have arouse wide concerns from new energy field.However,they have much lower torque density/thrust density compared with the same type PM machines.To overcome this challenge,electromagnetic-thermal coupled analysis is carried out with respect to water-cooled electrically-excited flux-switching linear machines(EEFSLM).The simulation results indicate that the conventional fixed copper loss method(FCLM)is no longer suitable for high thrust density design,since it is unable to consider the strong coupling between the electromagnetic and thermal performance.Hence,a multi-step electromagnetic-thermal joint optimisation method is proposed,which first ensures the consistency between the electromagnetic and thermal modelling and then considers the effect of different field/armature coil sizes.By using the proposed joint optimisation method,it is found that the combination of relatively large size of field coil and relatively low field copper loss is favourable for achieving high thrust force for the current EEFSLM design.Moreover,the thrust force is raised by 13-15%compared with using the FCLM.The electromagnetic and thermal performance of the EEFSLM is validated by the prototype test.

Comparative Study of Linear Variable Flux Reluctance Machine with Linear Wound Field Flux Reversal Machine

As members of doubly salient magnetless linear machines,linear variable flux reluctance(LVFR)and wound field flux reversal(LWFFR)machines inherit the merits of conventional magnetless linear machines such as low cost,high flux adjustment capability and high reliability.Furthermore,like linear switched reluctance machine,they have a very simple and compact long secondary,which are very attractive for long stroke applications.However,low force capability is their major defect.To solve this issue,new LVFR and LWFFR machine topologies were proposed in recent work,while lacking studies on their force improvement mechanism and further force evaluation.In this paper,LVFR and LWFFR machines with improved force performance are comparatively studied with the emphasis on their force capabilities.The operation principle of the two machines is analyzed based on magnetic field harmonics produced by flux modulation.Contributions of air-gap flux density harmonic components to no-load back electromagnetic forces of the two machines are analyzed and the average force equation is derived.Moreover,force capabilities of the both machines are investigated by means of the time-stepping finite-element analysis to verify the theoretical analysis.

Four-quadrant Force Control with Minimal Ripple for Linear Switched Reluctance Machines

Linear switch reluctance machine(LSRM)has been tried to act as an alternative generator for direct drive linear wave energy converter(WEC).Many researchers have proposed new topologies of LSRM to improve the power density,efficiency and reliability.However,the control methods for LSRM applied in direct drive WEC have been paid little attention,especially control methods considering the wave energy generator operating characteristics.In this paper,according to the generator control requirements of the direct drive WEC,force control algorithm for LSRM operating in four quadrants without a speed closed loop is put forward.The force ripple of LSRM is suppressed using force sharing function method.The four-quadrant control is easy to realize requiring only phase currents information.Simulation results validate the proposed method and indicate that LSRM is able to be used as the generator for direct drive WEC.

Finite-size analysis of eight-state continuous-variable quantum key distribution with the linear optics cloning machine

We propose a method to improve the secret key rate of an eight-state continuous-variable quantum key distribution（CVQKD） by using a linear optics cloning machine（LOCM）. In the proposed scheme, an LOCM is exploited to compensate for the imperfections of Bob＇s apparatus, so that the generated secret key rate of the eight-state protocol could be well enhanced. We investigate the security of our proposed protocol in a finite-size scenario so as to further approach the practical value of a secret key rate. Numeric simulation shows that the LOCM with reasonable tuning gain λ and transmittance τcan effectively improve the secret key rate of eight-state CVQKD in both an asymptotic limit and a finite-size regime.Furthermore, we obtain the tightest bound of the secure distance by taking the finite-size effect into account, which is more practical than that obtained in the asymptotic limit.

Performance Analysis of Nested-loop Secondary Linear Doubly-fed Machine Considering End Effects

Nested-loop secondary linear doubly-fed machine(NLS-LDFM) is a novel linear machine evolved from rotary brushless doubly-fed induction machine, which has a good application prospect in linear metro. In order to analyze the performance of NLS-LDFM, the mechanism and action rules of end effects are investigated in this paper. Firstly, the mechanism of static and dynamic end effects is analyzed in aspect of direct coupling, winding asymmetry and transient secondary current. Furthermore, based on the winding theory for short primary linear machines, the machine parameters are established qualitatively considering pulsating magnetic field of NLS-LDFM. Finally, the NLS-LDFM performance analysis is supplemented by the finite element algorithm(FEA) simulation and experiments under different operating conditions.

Balancing four-state continuous-variable quantum key distribution with linear optics cloning machine

We show that the secret key generation rate can be balanced with the maximum secure distance of four-state continuous-variable quantum key distribution（CV-QKD） by using the linear optics cloning machine（LOCM）. Benefiting from the LOCM operation, the LOCM-tuned noise can be employed by the reference partner of reconciliation to achieve higher secret key generation rates over a long distance. Simulation results show that the LOCM operation can flexibly regulate the secret key generation rate and the maximum secure distance and improve the performance of four-state CV-QKD protocol by dynamically tuning parameters in an appropriate range.

Temperature Prediction Model Identification Using Cyclic Coordinate Descent Based Linear Support Vector Regression

Temperature prediction plays an important role in ring die granulator control,which can influence the quantity and quality of production. Temperature prediction modeling is a complicated problem with its MIMO, nonlinear, and large time-delay characteristics. Support vector machine( SVM) has been successfully based on small data. But its accuracy is not high,in contrast,if the number of data and dimension of feature increase,the training time of model will increase dramatically. In this paper,a linear SVM was applied combing with cyclic coordinate descent( CCD) to solving big data regression. It was mathematically strictly proved and validated by simulation. Meanwhile,real data were conducted to prove the linear SVM model's effect. Compared with other methods for big data in simulation, this algorithm has apparent advantage not only in fast modeling but also in high fitness.

Quantitative Comparison of Modular Linear Permanent Magnet Vernier Machines with and without Partitioned Primary

A comparison of two modular linear permanent-magnet vernier(LPMV)machines is presented.A modular LPMV machine with a partitioned primary,which can significantly improve the modulation effect,is proposed.Benefitting from the partition design,the space conflict between the permanent magnet(PM)and the armature magnetic field is relieved.First,the topologies of modular LPMV machines with and without a partitioned primary are presented.Then,the effect of the partitioned primary on the modular LPMV machine is analyzed using flux modulation theory.Moreover,analytical expressions for the trapezoidal permeance are derived.In addition,the harmonic components,back electromotive forces,and thrust forces of the machines with and without the partitioned primary are comparatively analyzed.The results reveal that the thrust force density of the LPMV machine with a partitioned primary is increased by 32.3%.Finally,experiments are performed on a prototype machine for validation.

Electric Generators and Motors: an overview

Starting with Faraday’s law of electromagnetic induction in 1831,electric(electromagnetic)machines have been developed ever since as“assembles”of electric and magnetic coupled circuits that convert mechanical to electrical energy(in generators)and vice versa(in motors),via magnetic energy storage.Generators and motors are reversible.The Maxwell four equations(laws)later in 19th Century have prompted the rapid development of all basic(DC.brush and travelling field AC machines by 1900.Then by 1930 AC(alternating current)power(energy)systems evolved by connecting in parallel electric synchronous generators(with voltage boost and buck electric transformers for efficient AC power transmission lines)of rather constant frequency and voltage,driven by turbines(prime movers)that harness fossil(coal,gas or nuclear fuels),thermal or hydro energy.The last 50 years have witnessed a dramatic extension of generators power/unit,renewable energy generators and of variable speed AC motor drives in applications with variable output such as ventilators,pumps compressors,conveyors,orr-mills,electric transport(mobility),industrial automation,robotics,home appliances and info-gadgets.This formidable development,required by the need of more but cleaner energy,was mainly driven by power electronics,better materials,better modeling,design methodologies and digital control.This humble inaugural overview attempts to combine a brief history of electrical generators and motors with recent progress and trends in their design and control,for representative applications.

An Efficient Stabbing Based Intrusion Detection Framework for Sensor Networks

Intelligent Intrusion Detection System(IIDS)for networks provide a resourceful solution to network security than conventional intrusion defence mechanisms like a firewall.The efficiency of IIDS highly relies on the algorithm performance.The enhancements towards these methods are utilized to enhance the classification accuracy and diminish the testing and training time of these algorithms.Here,a novel and intelligent learning approach are known as the stabbing of intrusion with learning framework(SILF),is proposed to learn the attack features and reduce the dimensionality.It also reduces the testing and training time effectively and enhances Linear Support Vector Machine(l-SVM).It constructs an auto-encoder method,an efficient learning approach for feature construction unsupervised manner.Here,the inclusive certified signature(ICS)is added to the encoder and decoder to preserve the sensitive data without being harmed by the attackers.By training the samples in the preliminary stage,the selected features are provided into the classifier(lSVM)to enhance the prediction ability for intrusion and classification accuracy.Thus,the model efficiency is learned linearly.The multi-classification is examined and compared with various classifier approaches like conventional SVM,Random Forest(RF),Recurrent Neural Network(RNN),STL-IDS and game theory.The outcomes show that the proposed l-SVM has triggered the prediction rate by effectual testing and training and proves that the model is more efficient than the traditional approaches in terms of performance metrics like accuracy,precision,recall,F-measure,pvalue,MCC and so on.The proposed SILF enhances network intrusion detection and offers a novel research methodology for intrusion detection.Here,the simulation is done with a MATLAB environment where the proposed model shows a better trade-off compared to prevailing approaches.

Nutrient optimization for plant growth in Aquaponic irrigation using Machine Learning for small training datasets

With the recent trends in urban agriculture and climate change,there is an emerging need for alternative plant culture techniques where dependence on soil can be eliminated.Hydroponic and aquaponic growth techniques have proven to be viable alternatives,but the lack of efficient and optimal practices for irrigation and nutrient supply limits its applications on a large-scale commercial basis.The main purpose of this research was to develop statistical methods and Machine Learning algorithms to regulate nutrient concentrations in aquaponic irrigation water based on plant needs,for achieving optimal plant growth and promoting broader adoption of aquaponic culture on a commercial scale.One of the key challenges to developing these algorithms is the sparsity of data which requires the use of Bolstered error estimation approaches.In this paper,several linear and non-linear algorithms trained on relatively small datasets using Bolstered error estimation techniques were evaluated,for selecting the best method in making decisions regarding the regulation of nutrients in hydroponic environments.After repeated tests on the dataset,it was decided that Semi-Bolstered Resubstitution Error estimation technique works best in our case using Linear Support Vector Machine as the classifier with the value of penalty parameter set to one.A set of recommended rules have been prescribed as a Decision Support System,using the output of the Machine Learning algorithm,which have been tested against the results of the baseline model.Further,the positive impact of the recommended nutrient concentrationson plant growth in aquaponic environments has been elaborately discussed.

Design and modeling of a free-piston engine generator

Free-piston engine generators (FPEGs) can be applied as decarbonized range extenders for electric vehicles because of their high thermal efficiency, low friction loss, and ultimate fuel flexibility. In this paper, a parameter-decoupling approach is proposed to model the design of an FPEG. The parameter-decoupling approach first divides the FPEG into three parts: a two-stroke engine, an integrated scavenging pump, and a linear permanent magnet synchronous machine (LPMSM). Then, each of these is designed according to predefined specifications and performance targets. Using this decoupling approach, a numerical model of the FPEG, including the three aforementioned parts, was developed. Empirical equations were adopted to design the engine and scavenging pump, while special considerations were applied for the LPMSM. A finite element model with a multi-objective genetic algorithm was adopted for its design. The finite element model results were fed back to the numerical model to update the LPMSM with increased fidelity. The designed FPEG produced 10.2 kW of electric power with an overall system efficiency of 38.5% in a stable manner. The model provides a solid foundation for the manufacturing of related FPEG prototypes.

Application of a new SPA-SVM coupling method for QSPR study of electrophoretic mobilities of some organic and inorganic compounds

In this work, two chemometrics methods are applied for the modeling and prediction of electrophoretic mobilities of some organic and inorganic compounds. The successive projection algorithm, feature selection （SPA） strategy, is used as the descriptor selection and model development method. Then, the support vector machine （SVM） and multiple linear regression （MLR） model are utilized to construct the non-linear and linear quantitative structure-property relationship models. The results obtained using the SVM model are compared with those obtained using MLR reveal that the SVM model is of much better predictive value than the MLR one. The root-mean-square errors for the training set and the test set for the SVM model were 0.1911 and 0.2569, respectively, while by the MLR model, they were 0.4908 and 0.6494, respectively. The results show that the SVM model drastically enhances the ability of prediction in QSPR studies and is superior to the MLR model.