A High Thrust Force Spoke-Type Linear Permanent Magnet Vernier Machine with Reduced Thrust Force Ripple

Linear permanent magnet vernier machines (LP-MVMs) have become prevalent in direct-drive applications, such as wave energy harvesting systems and traction applications, owing to their distinctive merit of providing high thrust force at low speeds. In this paper, a novel structure of a double-sided spoke-type LPMVM is proposed, which takes advantage of the magnetic gearing effect. The proposed double-sided linear machine exploits spoke-type permanent magnets (PMs) and one of the stators is displaced as half of the stator tooth pitch to obtain the flux-focusing effect. The thrust force ripple of the proposed spoke-type LPMVM can be decreased by adjusting the stator end-teeth and mitigating the detrimental impact of the longitudinal effect. The proposed LPMVM with adjusted end-teeth offers a noteworthy potential in terms of high thrust force density, increased power factor, and reduced thrust force ripple, which makes it a suitable candidate for various direct-drive applications. The proposed LPMVM is compared with a conventional surface-mounted LPMVM and a spoke-type LP-MVM without adjusting end-teeth to verify the superiority of the new structure. Also, transient and steady-state thermal analyses of the proposed LPMVM are conducted to confirm its thermal stability. A two-dimensional finite element analysis (2D-FEA) is adopted to prove the outstanding characteristics of the proposed double-sided spoke-type linear vernier structure

Linear Permanent Magnet Vernier Generators for Wave Energy Applications: Analysis, Challenges, and Opportunities

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).Harvesting energy from waves as a substantial resource of renewable energy has attracted much attention in recent years. Linear permanent magnet vernier generators (LPMVGs) have been widely adopted in wave energy applications to extract clean energy from oceans. Linear PM vernier machines perform based on the magnetic gearing effect, allowing them to offer high power/force density at low speeds. The outstanding feature of providing high power capability makes linear vernier generators more advantageous compared to linear PM synchronous counterparts used in wave energy conversion systems. Nevertheless, they inherently suffer from a poor power factor arising from their considerable leakage flux. Various structures and methods have been introduced to enhance their performance and improve their low power factor. In this work, a comparative study of different structures, distinguishable concepts, and operation principles of linear PM vernier machines is presented. Furthermore, recent advancements and innovative improvements have been investigated. They are categorized and evaluated to provide a comprehensive insight into the exploitation of linear vernier generators in wave energy extracting systems. Finally, some significant structures of linear PM vernier generators are modeled using two-dimensional finite element analysis (2D-FEA) to compare their electromagnetic characteristics and survey their performance.Peer reviewe

Engaging soft computing in material and modeling uncertainty quantification of dam engineering problems

Due to complex nature of nearly all infrastructures (and more specifically concrete dams), the uncertainty quantification is an inseparable part of risk assessment. Uncertainties might be propagated in different aspects depending on their relative importance such as epistemic and aleatory, or spatial and temporal. The objective of this paper is to focus on the material and modeling uncertainties, and to couple them with soft computing techniques aiming to reduce the computational burden of the conventional Monte Carlo-based finite element simulations. Several scenarios are considered in which the concrete and foundation material properties, the water level, and the dam geometry are assumed as random variables. Five soft computing techniques (i.e., random forest, boosted regression trees, multi-adaptive regression splines, artificial neural networks, and support vector machines) are employed to predict various quantities of interest based on different training sizes. It is argued that the artificial neural network is the most accurate algorithm in majority of cases, with enough accuracy as to be useful in reliability analysis as a complement to numerical models. The results with 200 samples in the training set are enough for reaching useful accuracy in most cases. For the simple prediction tasks, the results were predicted with less than 1% error. It is observed that increasing the number of input parameters increases the prediction error. The partial dependence plots provided most sensitive variables in dam design, which were consistent with the physics of the problem. Finally, several practical recommendations are provided for future applications

Investigation into the Thermal Behavior and Loadability Characteristic of a YASA-AFPM Generator via an Improved 3-D Coupled Electromagnetic-Thermal Approach

The objective of this paper is to investigate the thermal behaviour and loadability characteristic of a yokeless and segmented armature axial-flux permanent-magnet (YASA-AFPM) generator, which uses an improved 3-D coupled electromagnetic-thermal approach. Firstly, a 1-kW YASA-AFPM generator is modelled and analysed by using the proposed approach; the transient and steady-state temperatures of different parts of the generator are determined. To improve the modelling accuracy, the information is exchanged between the thermal and electromagnetic models at each step of the co-simulation, considering both the accurate calculation of losses and the impacts of temperature rise on the temperature-dependent characteristics of the materials. Then, by using the proposed approach, the impact of the slot opening width and the turn number of stator segments on the generator loadability are investigated. After that, the experimental tests are performed. The results reveal the effectiveness and accuracy of the approach to predict the machine loadability and thermal behavior

Performance Based Earthquake Engineering of Concrete Dams

The main objective of this thesis is to develop a framework for performance based earthquake engineering (PBEE) of concrete dams. To pursue this goal, this study first develops an extended and quantitative version of potential failure mode analysis (PFMA) for concrete dams. Different failure modes are investigated for all types of concrete dams. A Matlab-based code is developed for probabilistic performance assessment of concrete dams (PPACD). This code is used for assessment of concrete dams within the context of PBEE. A probabilistic seismic demand model (PSDM) is proposed for concrete dams based on cloud analysis methodology. The outcome of PSDM is selection of optima intensity measure (IM) parameters for gravity dams. Then, the sensitivity and uncertainty of dam-foundation system is quantified under the mixed-mode fracture of zero-thickness interface joint element. Capacity and fragility curves are derived for most sensitive random variables. This research also examined the performance of the dam under incremental dynamic analysis (IDA). First, the anatomy of a single-record IDA is studied and contrasted by framed structures. Then, the collapse fragility curves are derived for single and multiple-component ground motions. The impact of epistemic uncertainty is investigated in addition to the aleatoric one. Finally, a multi-scale damage index (DI) is proposed for gravity dams which is a function of crest displacement, crack ratio, and dissipated energy. Using this hybrid DI, a computationally simple but effective methodology is proposed for progressive failure analysis of dams. In all cases, first the methodology is discussed and then, a numerical example illustrates the details

Developing a Library of Shear Walls Database and the Neural Network Based Predictive Meta-Model

There is a large amount of useful information from past experimental tests, which are usually ignored in test-setup for the new ones. Variation of assumptions, materials, test procedures, and test objectives make it difficult to choose the right model for validation of the numerical models. Results from different experiments are sometimes in conflict with each other, or have minimum correlation. Furthermore, not all these information are easily accessible for researchers and engineers. Therefore, this paper presents the results of a comprehensive study on different experimental models for steel plate and reinforced concrete shear walls. A unique library of up to 13 parameters (mechanical properties and geometric characteristics) affecting the strength, stiffness and drift ratio of the shear walls are gathered including their sensitivity analysis. Next, a predictive meta-model is developed based on artificial neural network. It is capable of forecasting the responses for any desired shear wall with good accuracy. The proposed network can be used to as an alternative to the nonlinear numerical simulations or expensive experimental test.</p

SURVEYING THE IMPACT OF INTENTIONAL ORGANIZATIONAL FORGETTING ON TECHNICAL INNOVATIONS

Abstract The description of correlation is methodology of this research. The main purpose of this study is surveying the Impact of Intentional Organizational Forgetting on technical innovations. The population of this research includes of all small and middle established employees who work in Ardabil industrial cities. Sampling method is the simple random method which based on Cochran formula, 132 companies were selected as the sample size. This data collected by questionnaire. We have used multi variable regression for analyzing and meaningfulness of the data. The findings show that any dimensions of intentional organizational forgetting, learning relaxation and avoiding bad habits have the power of explaining and predicting technical innovations