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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.

One of the keys to the success of the fourth industrial revolution (Industry 4.0) is to empower machinery with cyber–physical systems connectivity. The digital twin (DT) offers a promising solution to tackle the challenges for realizing digital and smart manufacturing which has been successfully projected in many scenes. Electrical machines and drive systems, as the core power providers in many appliances and industrial equipment, are supposed to be reinforced on the verge of Industry 4.0 in the fields of design optimization, fault prognostic and coordinated control. Therefore, this paper aims to investigate the DT modelling method and the applications in electrical drive systems. Firstly, taking the high-speed permanent-magnet machine drive system as an example, multi-disciplinary design fundamentals and technologies, aiming at building initial mechanism and simulation models, are reviewed. The state-of-the-art of DT technologies is figured out to serve for high-precision and multi-scale dynamic modelling, by which a framework for DT models of electrical drive systems is presented. More importantly, fault diagnosis and optimization strategies of electrical drive systems in the decision and application layer are also discussed for the DT models, followed by the conclusions presenting open questions and possible directions.

This investigation presents a topology optimization method for the design of lightweight serial robots for industrial applications such as welding robots and painting robots. It might be numerically efficient to perform topology optimization of a robot structure by dividing the problem into part-level optimization problems. However, the robot structure whose parts are separately optimized is not necessarily the optimized structure in the system level. For example, a robot whose parts are separately designed to have maximum stiffness-to-mass ratio cannot have the maximum stiffness in the system level. This is because it is impossible to know in the stage of the problem formulation how the total mass should be divided into each part to have maximized system stiffness. To deal with this, a metamodel relating the stiffness and the mass usage is constructed in each part-level optimization problem. The proper division of a mass in the part level is determined by solving the system-level optimization problem which is formulated by using the part-level metamodels. Optimized robot structures obtained by the proposed approach are shown to have performances close to system-level optimized ones in test problems with two- and three-dimensional static and dynamic cases. Based on the proposed idea, topology optimization of a painting robot is performed; a base frame, a lower frame and an upper frame of the robot are optimized to lower the maximum system strain energy during the motion.

A thermoelectric generator (TEG) is a clean electricity generator from a heat source, usually waste heat. However, it is not as widely utilized as other electricity generators due to low conversion efficiency from heat to electricity. One approach is a system-level net power optimization for a TEG system composed of TEGs, heat sink, and fans. In this paper, we propose airflow reuse after cooling preceding TEGs to maximize system net power. For the accurate system net power, we model the TEG system, air, and heat source with proper dimension and material characteristics, and simulate with a computational fluid dynamics program. Next, the TEG power generation and the fan power consumption are calculated in consideration of the Seebeck coefficient and internal electrical resistance varying with hot and cold side temperatures. Finally, we find the optimal number of TEGs and fan speed generating the most efficient system net power in various TEG systems. The results show that the system with a side fan with a specific number of TEGs provides a system net power up to 58.6% higher than when with a top fan. The most efficient system net power with the side fan increases up to four TEGs generating 1.907 W at 13,000 RPM.

This review paper provides a comprehensive examination of current strategies and technical considerations for reducing return temperatures in district heating (DH) systems, aiming to enhance the utilization of available thermal energy. Return temperature, a parameter indirectly influenced by various system-level factors, cannot be adjusted directly but requires careful management throughout the design, commissioning, operation, and control phases. This paper explores several key factors affecting return temperature, including DH network, heat storage, and control strategies as well as the return temperature effect on the heat source. This paper also considers the influence of non-technical aspects, such as pricing strategies and maintenance practices, on system performance. The discussion extends to the complex interplay between low return temperatures and temperature differences, and between operational temperature schemes and economic considerations. Concluding remarks emphasize the importance of adopting a holistic approach that integrates technical, operational, and economic factors to improve DH system efficiency. This review highlights the need for comprehensive system-level optimization, effective management of system components, and consideration of unique heat production characteristics. By addressing these aspects, this study provides a framework for advancing DH system performance through optimized return temperature management.

The sigma-delta modulator (SDM) is one of the well-established data converter architectures. It is well-known for achieving a high signal-to-noise ratio (SNR). In the SDM, the integrators in the loop filter could suffer from overloading if the signal swing exceeds its maximum level, which leads to performance and SNR degradation. Thus, scaling the system coefficients is needed, such that there is no overloading for the integrators. In this work, we present a systematic general method that could be used for scaling the signal swings in the continuous-time low-pass sigma-delta modulator (SDM). The proposed method can be applied to any continuous-time low-pass SDM architecture, and it includes the scaling of all the possible combinations of the system coefficients. Moreover, an open-source Simulink-based toolbox that includes the systematic method is presented. This toolbox could help the designer to execute the scaling process and the simulations in an efficient way. In addition to that, a design example is discussed to illustrate the proposed method, wherein the presented toolbox is used for simulations, and the simulation results are shown.

Switched Reluctance Machines (SRMs) are gaining increasing traction within the industrial sector, primarily due to their inherently simple and robust structure. Nevertheless, SRMs are characterized by two major drawbacks—high torque ripple and strong radial forces—both of which render them less suitable for applications requiring smooth operation, such as Electric Vehicles (EVs). To address these limitations, researchers and designers focus on optimizing these critical performance metrics during the design phase. In recent years, the concept of System-Level Design Optimization (SLDOM) has been introduced and applied to SRM drive systems, where both the machine and the controller are simultaneously considered within the optimization framework. This integrated approach has shown significant improvements in mitigating the aforementioned issues. This paper aims to review the existing literature concerning the SLDOM applied to SRMs, highlighting the key methodologies and findings from studies conducted in recent years. Despite its promising outcomes, the adoption of SLDOM remains limited due to its high computational cost and complexity. In response to these challenges, the paper discusses complementary techniques used to enhance the optimization process, such as search space and computational time reduction strategies, along with the associated challenges and potential solutions. Finally, two critical directions for future research are identified, which are expected to influence the development of the SLDOM and its application to SRMs in the coming years.

The performance of thermoelectric modules for energy-harvesting applications is investigated, and a model is presented to predict module performance. Derived from energy conservation equations, the model predicts module performance by solving for the temperatures at both ends of the thermoelectric materials within a module. Unlike traditional methods, the model accounts for the effect of electrical current with respect to the load resistance by considering additional heat transfer by Joule heating and the Peltier effect. This establishes a nonlinear quadratic form of temperatures which can be solved by an iterative numerical solution. The model is extended to predict the performance of energy-harvesting systems, which may include connection of multiple thermoelectric modules in series to meet the necessary power requirements. However, a key issue with multiple module connection is the power reduction that arises when there are significant differences in module properties and/or the corresponding external conditions to which each individual module is exposed. Power reduction is thus investigated, as in some cases the overall power output for multiple modules can be less than the power output of a single module. For validation and comparison of the model, experimental support is provided for the case of two commercial thermoelectric modules connected in series. The model also provides optimum load resistances, and a system optimization of the number of modules for a designated heat sink to maximize power generation. The overarching goal of this work is to provide performance prediction and optimization considerations for actual thermoelectric energy-harvesting systems.

This chapter introduces distributed embedded wireless sensor networks (EWSN) from an optimization perspective and explores optimization strategies employed in EWSNs at different design levels to meet application requirements. It first presents a typical WSN architecture and architectural-level optimizations. The chapter describes sensor node component-level optimizations and tunable parameters. Next, it discusses data link-level medium access control (MAC) optimizations and network-level routing optimizations, and operating system (OS)-level optimizations. After presenting these optimization techniques, the chapter focuses on dynamic optimizations for WSNs. There exists much previous work on dynamic optimizations, but most previous work targets the processor or cache subsystem in computing systems. WSN dynamic optimizations present additional challenges because of a unique design space, stringent design constraints, and varying operating environments. The chapter also discusses the current state of the art in dynamic optimizations such as dynamic voltage and frequency scaling (DVFS) and dynamic network reprogramming.

This study examines the performance of asymmetric three-phase distribution systems under the influence of FACT deives such as a static VAR compensator (SVC) and a unified power controller (UPC). Each suggested device’s operating principle is developed in this paper in order to provide the best model to be used in the power flow analysis. The performance of the IEEE-13 bus imbalanced distribution model is investigated using the Newton-Raphson method. To improve network asymmetry, an optimization analysis is carried out to ascertain how each proposed device will work. In this paper, the mode of operation of each device is determined every hour to flow the changing in loads according to their daily load curve. The genetic algorithm (GA) is utilized to determine the contribution of the unified flow controller to balance the loads between phases. The multi-objective function is constructed to combine the total system losses and the average voltage unbalance coefficient. The paper provides an optimal dynamic technique to optimally operate the unified power flow controller installed in the unbalanced three-phase distribution system.