Flux Weakening Strategy Optimization for Five-Phase PM Machine with Concentrated Windings

The paper applies an Efficient Global Optimization method (EGO) to improve the efficiency, in flux weakening region, of a given 5-phase Permanent Magnet (PM) machine. An optimal control for the four independent currents is thus defined. Moreover, a modification proposal of the machine geometry is added to the optimization process of the global drive. The effectiveness of the method allows solving the challenge which consists in taking into account inside the control strategy the eddy-current losses in magnets and iron. In fact, magnet losses are a critical point to protect the machine from demagnetization in flux-weakening region. But these losses, which highly depend on magnetic state of the machine, must be calculated by Finite Element Method (FEM) to be accurate. The FEM has the drawback to be time consuming. It is why a direct optimization using FEM is critical. EGO method, using sparingly FEM, allows to find a feasible solution to this hard optimization problem of control and design of multi-phase drive

Comparison and Design Optimization of a Five-Phase Flux-Switching PM Machine for In-Wheel Traction Applications

A comparative study of five-phase outer-rotor flux-switching permanent magnet (FSPM) machines with different topologies for in-wheel traction applications is presented in this paper. Those topologies include double-layer winding, single-layer winding, C-core, and E-core configurations. The electromagnetic performance in the low-speed region, the flux-weakening capability in the high-speed region, and the fault-tolerance capability are all investigated in detail. The results indicate that the E-core FSPM machine has performance advantages. Furthermore, two kinds of E-core FSPM machines with different stator and rotor pole combinations are optimized, respectively. In order to reduce the computational burden during the large-scale optimization process, a mathematical technique is developed based on the concept of computationally efficient finite-element analysis. While a differential evolution algorithm serves as a global search engine to target optimized designs. Subsequently, multiobjective tradeoffs are presented based on a Pareto-set for 20 000 candidate designs. Finally, an optimal design is prototyped, and some experimental results are given to confirm the validity of the simulation results in this paper

Study on the impact of uncertain design parameters on the perfomances of a permanent magnet assisted synchronous reluctance motor

In this paper, deterministic and robust design optimizations of a permanent magnet assisted synchronous reluctance machine were performed to study the impact of different uncertain input parameters on the design. These optimizations were carried out using a surrogate model based on 2-D finite element simulations. Different robust optimizations considering geometric and magnetic uncertain parameters were compared to the deterministic optimization. It was noticed that both geometrical and magnetic properties tolerances greatly impact the machines' performances, where the magnetic properties had a more significant impact on the mean torque. In such a case, robust optimization is essential to find optimal and robust electric motors designs.Comment: arXiv admin note: substantial text overlap with arXiv:2211.1613

Advanced RF and Microwave Design Optimization: A Journey and a Vision of Future Trends

In this paper, we outline the historical evolution of RF and microwave design optimization and envisage imminent and future challenges that will be addressed by the next generation of optimization developments. Our journey starts in the 1960s, with the emergence of formal numerical optimization algorithms for circuit design. In our fast historical analysis, we emphasize the last two decades of documented microwave design optimization problems and solutions. From that retrospective, we identify a number of prominent scientific and engineering challenges: 1) the reliable and computationally efficient optimization of highly accurate system-level complex models subject to statistical uncertainty and varying operating or environmental conditions; 2) the computationally-efficient EM-driven multi-objective design optimization in high-dimensional design spaces including categorical, conditional, or combinatorial variables; and 3) the manufacturability assessment, statistical design, and yield optimization of high-frequency structures based on high-fidelity multi-physical representations. To address these major challenges, we venture into the development of sophisticated optimization approaches, exploiting confined and dimensionally reduced surrogate vehicles, automated feature-engineering-based optimization, and formal cognition-driven space mapping approaches, assisted by Bayesian and machine learning techniques.ITESO, A.C

A review of design optimization methods for electrical machines

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. Electrical machines are the hearts of many appliances, industrial equipment and systems. In the context of global sustainability, they must fulfill various requirements, not only physically and technologically but also environmentally. Therefore, their design optimization process becomes more and more complex as more engineering disciplines/domains and constraints are involved, such as electromagnetics, structural mechanics and heat transfer. This paper aims to present a review of the design optimization methods for electrical machines, including design analysis methods and models, optimization models, algorithms and methods/strategies. Several efficient optimization methods/strategies are highlighted with comments, including surrogate-model based and multi-level optimization methods. In addition, two promising and challenging topics in both academic and industrial communities are discussed, and two novel optimization methods are introduced for advanced design optimization of electrical machines. First, a system-level design optimization method is introduced for the development of advanced electric drive systems. Second, a robust design optimization method based on the design for six-sigma technique is introduced for high-quality manufacturing of electrical machines in production. Meanwhile, a proposal is presented for the development of a robust design optimization service based on industrial big data and cloud computing services. Finally, five future directions are proposed, including smart design optimization method for future intelligent design and production of electrical machines

Magnetic Material Modelling of Electrical Machines

The need for electromechanical energy conversion that takes place in electric motors, generators, and actuators is an important aspect associated with current development. The efficiency and effectiveness of the conversion process depends on both the design of the devices and the materials used in those devices. In this context, this book addresses important aspects of electrical machines, namely their materials, design, and optimization. It is essential for the design process of electrical machines to be carried out through extensive numerical field computations. Thus, the reprint also focuses on the accuracy of these computations, as well as the quality of the material models that are adopted. Another aspect of interest is the modeling of properties such as hysteresis, alternating and rotating losses and demagnetization. In addition, the characterization of materials and their dependence on mechanical quantities such as stresses and temperature are also considered. The reprint also addresses another aspect that needs to be considered for the development of the optimal global system in some applications, which is the case of drives that are associated with electrical machines

On-the-fly adaptivity for nonlinear twoscale simulations using artificial neural networks and reduced order modeling

A multi-fidelity surrogate model for highly nonlinear multiscale problems is proposed. It is based on the introduction of two different surrogate models and an adaptive on-the-fly switching. The two concurrent surrogates are built incrementally starting from a moderate set of evaluations of the full order model. Therefore, a reduced order model (ROM) is generated. Using a hybrid ROM-preconditioned FE solver, additional effective stress-strain data is simulated while the number of samples is kept to a moderate level by using a dedicated and physics-guided sampling technique. Machine learning (ML) is subsequently used to build the second surrogate by means of artificial neural networks (ANN). Different ANN architectures are explored and the features used as inputs of the ANN are fine tuned in order to improve the overall quality of the ML model. Additional ANN surrogates for the stress errors are generated. Therefore, conservative design guidelines for error surrogates are presented by adapting the loss functions of the ANN training in pure regression or pure classification settings. The error surrogates can be used as quality indicators in order to adaptively select the appropriate -- i.e. efficient yet accurate -- surrogate. Two strategies for the on-the-fly switching are investigated and a practicable and robust algorithm is proposed that eliminates relevant technical difficulties attributed to model switching. The provided algorithms and ANN design guidelines can easily be adopted for different problem settings and, thereby, they enable generalization of the used machine learning techniques for a wide range of applications. The resulting hybrid surrogate is employed in challenging multilevel FE simulations for a three-phase composite with pseudo-plastic micro-constituents. Numerical examples highlight the performance of the proposed approach

Application-oriented robust design optimization method for batch production of permanent-magnet motors

© 2017 IEEE. From the perspective of industrial production, the design and optimization of electrical machines are application oriented, including maximizing production quality and minimizing production cost in terms of different manufacturing conditions. To achieve these goals, this study presents an efficient application-oriented robust design optimization method for permanent-magnet (PM) motors. The method consists of two main contributions. The first one is the development of an overall optimization strategy, including qualitative and quantitative analyses to provide possible options for an application. Multiphysics analysis, uncertainty analysis, production cost, and optimization models need to be investigated. The second one proposes a multilevel optimization method for the high-dimensional robust design problem of each option. To illustrate the advantages of the proposed method, PM motorswith soft magnetic composite cores are investigated for domestic applications. The design optimization is conducted in terms of three motor options and three batch production volumes for both conventional deterministic and robust approaches, and it consists of 18 high-dimensional multiphysics optimization problems in total. Main optimization results are presented and discussed. Experimental and simulation results are presented to validate the effectiveness of the proposed models and methods

Physics-Driven ML-Based Modelling for Correcting Inverse Estimation

When deploying machine learning estimators in science and engineering (SAE) domains, it is critical to avoid failed estimations that can have disastrous consequences, e.g., in aero engine design. This work focuses on detecting and correcting failed state estimations before adopting them in SAE inverse problems, by utilizing simulations and performance metrics guided by physical laws. We suggest to flag a machine learning estimation when its physical model error exceeds a feasible threshold, and propose a novel approach, GEESE, to correct it through optimization, aiming at delivering both low error and high efficiency. The key designs of GEESE include (1) a hybrid surrogate error model to provide fast error estimations to reduce simulation cost and to enable gradient based backpropagation of error feedback, and (2) two generative models to approximate the probability distributions of the candidate states for simulating the exploitation and exploration behaviours. All three models are constructed as neural networks. GEESE is tested on three real-world SAE inverse problems and compared to a number of state-of-the-art optimization/search approaches. Results show that it fails the least number of times in terms of finding a feasible state correction, and requires physical evaluations less frequently in general.Comment: 19 pages, the paper is accepted by Neurips 2023 as a spotligh

Efficient design optimization of high-performance MEMS based on a surrogate-assisted self-adaptive differential evolution

High-performance microelectromechanical systems (MEMS) are playing a critical role in modern engineering systems. Due to computationally expensive numerical analysis and stringent design specifications nowadays, both the optimization efficiency and quality of design solutions become challenges for available MEMS shape optimization methods. In this paper, a new method, called self-adaptive surrogate model-assisted differential evolution for MEMS optimization (ASDEMO), is presented to address these challenges. The main innovation of ASDEMO is a hybrid differential evolution mutation strategy combination and its self-adaptive adoption mechanism, which are proposed for online surrogate model-assisted MEMS optimization. The performance of ASDEMO is demonstrated by a high-performance electro-thermo-elastic micro-actuator, a high-performance corrugated membrane microactuator, and a highly multimodal mathematical benchmark problem. Comparisons with state-of-the-art methods verify the advantages of ASDEMO in terms of efficiency and optimization ability