Efficiency Optimization Strategy of Permanent Magnet Synchronous Motor for Electric Vehicles Based on Energy Balance

This paper presents an efficiency optimization controller for a permanent magnet synchronous motor (PMSM) of an electric vehicle. A new loss model is obtained based on the permanent magnet synchronous motor’s energy balance equation utilizing the theory of the port-controlled Hamiltonian system. Since the energy balance equation is just the power loss of the PMSM, which provides great convenience for us to use the energy method for efficiency optimization. Then, a new loss minimization algorithm (LMA) is designed based on the new loss model by adjusting the ratio of the excitation current in the d–q axis. Moreover, the proposed algorithm is achieved by the principle of the energy shape method of the Hamiltonian system. Simulations are finally presented to verify effectiveness. The main results of these simulations indicate that the dynamic performance of the drive is maintained and the efficiency increase is up to about 7% compared with the id=0 control algorithm, and about 4.5% compared with the conventional LMA at a steady operation of a PMSM.

Simulation of iron losses in induction machines using an iron loss model for rotating magnetization loci in no electrical steel

Purpose Various iron loss models can be used for the simulation of electrical machines. In particular, the effect of rotating magnetic flux density at certain geometric locations in a machine is often neglected by conventional iron loss models. The purpose of this paper is to compare the adapted IEM loss model for rotational magnetization that is developed within the context of this work with other existing models in the framework of a finite element simulation of an exemplary induction machine. Design/methodology/approach In this paper, an adapted IEM loss model for rotational magnetization, developed within the context of the paper, is implemented in a finite element method simulation and used to calculate the iron losses of an exemplary induction machine. The resulting iron losses are compared with the iron losses simulated using three other already existing iron loss models that do not consider the effects of rotational flux densities. The used iron loss models are the modified Bertotti model, the IEM-5 parameter model and a dynamic core loss model. For the analysis, different operating points and different locations within the machine are examined, leading to the analysis of different shapes and amplitudes of the flux density curves. Findings The modified Bertotti model, the IEM-5 parameter model and the dynamic core loss model underestimate the hysteresis and excess losses in locations of rotational magnetizations and low-flux densities, while they overestimate the losses for rotational magnetization and high-flux densities. The error is reduced by the adapted IEM loss model for rotational magnetization. Furthermore, it is shown that the dynamic core loss model results in significant higher hysteresis losses for magnetizations with a high amount of harmonics. Originality/value The simulation results show that the adapted IEM loss model for rotational magnetization provides very similar results to existing iron loss models in the case of unidirectional magnetization. Furthermore, it is able to reproduce the effects of rotational flux densities on iron losses within a machine simulation.

Effects of Sparganii Rhizoma on Osteoclast Formation and Osteoblast Differentiation and on an OVX-Induced Bone Loss Model

Postmenopausal osteoporosis is caused by an imbalance between osteoclasts and osteoblasts and causes severe bone loss. Osteoporotic medicines are classified into bone resorption inhibitors and bone formation promoters according to the mechanism of action. Long-term use of bisphosphonate and selective estrogen receptor modulators (SERMs) can cause severe side effects in postmenopausal osteoporosis patients. Therefore, it is important to find alternative natural products that reduce osteoclast activity and increase osteoblast formation. Sparganii Rhizoma (SR) is the dried tuberous rhizome of Sparganium stoloniferum Buchanan-Hamilton and is called “samreung” in Korea. However, to date, the effect of SR on osteoclast differentiation and the ovariectomized (OVX)-induced bone loss model has not been reported. In vitro, tartrate-resistant acid phosphatase (TRAP) staining, western blots, RT-PCR and other methods were used to examine the effect of SR on osteoclast differentiation and osteoblasts. In vivo, we confirmed the effect of SR in a model of OVX-induced postmenopausal osteoporosis. SR inhibited osteoclast differentiation and decreased the expression of TNF receptor-associated factor 6 (TRAF6), nuclear factor of activated T cells 1 (NFATc1) and c-Fos pathway. In addition, SR stimulates osteoblast differentiation and increased protein expression of the bone morphogenetic protein 2 (BMP-2)/SMAD signaling pathway. Moreover, SR protected against bone loss in OVX-induced rats. Our results appear to advance our knowledge of SR and successfully demonstrate its potential role as a osteoclastogenesis-inhibiting and osteogenesis-promoting herbal medicine for the treatment of postmenopausal osteoporosis.

Investigating Automated Hyper-Parameter Optimization for a Generalized Path Loss Model

This work aims at developing a generalized and optimized path loss model that considers rural, suburban, urban, and urban high rise environments over different frequencies, for use in the Heterogenous Ultra Dense Networks in 5G. Five different machine learning algorithms were tested on four combined datasets, with a sum of 12369 samples in which their hyper-parameters were automatically optimized using Bayesian optimization, HyperBand and Asynchronous Successive Halving (ASHA). For the Bayesian optimization, three surrogate models (the Gaussian Process, Tree Structured Parzen Estimator and Random Forest) were considered. To the best of our knowledge, few works have been found on automatic hyper-parameter optimization for path loss prediction and none of the works used the aforementioned optimization techniques. Differentiation among the various environments was achieved by the assignment of the clutter height values based on International Telecommunication Union Recommendation (ITU-R) P.452-16. We also included the elevation of the transmitting antenna position as a feature so as to capture its effect on path loss. The best machine learning model observed is K Nearest Neighbor (KNN), achieving mean Coefficient of Determination (R2), average Mean Absolute Error (MAE) and mean Root Mean Squared Error (RMSE) values of 0.7713, 4.8860dB, and 6.8944dB, respectively, obtained from 100 different samplings of train set and test set. Results show that machine learning can also be used to develop path loss models that are valid for a certain range of distances, frequencies, antenna heights, and environment types. HyperBand produced hyper-parameter configurations with the highest accuracy in most of the algorithms.

Searching for the Most Suitable Loss Model Set for Subsonic Centrifugal Compressors Using an Improved Method for Off-Design Performance Prediction

This work investigates loss model sets based on empirical loss correlations for subsonic centrifugal compressors. These loss models in combination with off-design performance prediction algorithms make up an essential tool in predicting off-design behaviour of turbomachines. This is important since turbomachines rarely work under design conditions. This study employs an off-design performance prediction algorithm based on an iterative process from Galvas. Modelling of ten different loss mechanisms and physical phenomena is involved in this approach and is thoroughly described in this work. Geometries of two subsonic compressors were reconstructed and used in the evaluation of individual loss correlations in order to obtain a suitable loss model. Results of these variations are compared to experimental data. In addition, 4608 loss model sets were created by taking all possible combinations of individual loss estimations from which three promising candidates were selected for further investigation. Finally, off-design performance of both centrifugal compressors was computed. These results were compared to experimental data and to other loss model sets from literature. The newly composed loss model set No. 2137 approximates experimental data over a 21.2% better in relative error than the recent Zhang set and nearly a 36.7% better than the outdated Oh’s set. Therefore, set No. 2137 may contribute to higher precision of centrifugal turbomachines’ off-design predictions in the upcoming research.

Call Blocking Probabilities under a Probabilistic Bandwidth Reservation Policy in Mobile Hotspots

In this paper we study a mobility-aware call admission control algorithm in a mobile hotspot. To this end, a vehicle is considered which has an access point with a fixed capacity. The vehicle alternates between stop and moving phases. When the vehicle is in the stop phase, it services new and handover calls by prioritizing them via a probabilistic bandwidth reservation (BR) policy. Based on this policy, new handover calls may enter the reservation space with a predefined probability. When the vehicle is in the moving phase, it services new calls only. In that phase, two different policies are considered: (a) the classical complete sharing (CS) policy, where new calls are accepted in the system whenever there exists available bandwidth, and (b) the probabilistic BR policy. Depending on the selected policy in the moving phase, we propose the probabilistic BR loss model (if the CS policy is selected) and the generalized probabilistic BR loss model (if the probabilistic BR policy is selected). In both stop and moving phases, where the call arrival process is Poisson, calls require a single bandwidth unit in order to be accepted in the system, while the service time is exponentially distributed. To analytically determine call blocking probabilities and the system’s utilization, we propose efficient iterative algorithms based on two-dimensional Markov chains. The accuracy of the proposed algorithms is verified via simulation.

Development of a New Loss Model for Turbomachinery Labyrinth Seals

The preliminary design of labyrinth seals requires a fast and accurate estimate of the leakage flow. While the conventional bulk flow models can quickly predict labyrinth seal discharge characteristics, they lack the accuracy and pragmatism of modern CFD technique and vice-a-versa. This paper presents a new 1D loss model for straight-through gas labyrinth seals that can provide quick seal leakage flow predictions with CFD-equivalent accuracy. The present seal loss model is developed using numerical experimentation technique. Multiple CFD computations are conducted on straight-through labyrinth seal geometries for a range of pressure ratios. A distinct post-processing methodology is developed to extract the through-flow stream tube in seal. Total pressure losses and flow area variations experienced by the flow in seal stream-tube are systematically accounted for based on the well-known knife-to-knife (K2K) methodology. Regression analyses are conducted on the trends of variations of loss and area coefficients to derive the independent pressure loss and flow area correlations. These novel correlations can predict the bulk leakage flow rate, windage flow rate and inter-knife static pressures over a wide range of variation of flow and geometry parameters. Validation study shows that the leakage mass flow rate predicted by this model is accurate within ±8% of measured test data. This fast and accurate model can be employed for various applications such as, in seal design-analysis workflows, for secondary air system (SAS) performance analysis and for the rotor-dynamic and aeroelastic assessments of seals.

Simulating energetic particle losses in JET plasmas with a reverse integration biasing scheme

An integrated energetic particle transport model has been constructed in JET plasmas constrained by experimental fast ion loss measurements. The model incorporates a synthetic fast ion loss detector identical to JET's thin-foil Faraday cup fast ion loss detector array. The loss model combines analyses from the TRANSP and ORBIT-kick codes with enhanced features for producing the synthetic diagnostic. Extensions to the ORBIT code framework allow a full-orbit representation within the vacuum region that can map particles directly to an installed detector geometry. Since synthetic fast ion loss detectors are plagued by weak loss statistics, a novel reverse integration biasing scheme has been implemented to boost computational efficiency. The model is validated against experimental loss measurements induced by long-lived kink modes and is found to be in good agreement. This confirms the development of a fully integrated transport/loss model which can be quantitatively verified against experiment allowing for future validation and predictive studies. The model is particularly useful for more complicated plasma scenarios that involve multiple fast ion species such as JET's 2021 DT-campaign.