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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.

High speed permanent magnet synchronous motor (HSPMSM) is an important driving motor for electric vehicles. Compared with the electric motor operated under normal speed, it is critical to reduce the torque ripple, vibration, and noise of the HSPMSM. Furthermore, The air-gap flux density total harmonic distortion and electromotive force total harmonic distortion determine by torque performance and vibration noise. This article uses analytical methods to analyze the influence of permanent magnet sinusoidal segmentation (PMS) on the electromagnetic performance of surface-mounted HSPMSM. The finite element method is used to calculate the influence of different permanent magnet segmentation numbers, different adjacent permanent magnet decreasing thickness, and different center PM width on the electromagnetic performance of permanent magnet sinusoidal segmented surface-mounted high-speed permanent magnet synchronous motor (M2). Comparing the effectiveness of PMS in surface-mounted HSPMSM and interior HSPMSM. Furthermore, the rotor dynamics analysis and vibration noise analysis on M2 are performed, and the influence of PMS on the stress and vibration noise of both motors’ rotors is summarized. This article proposes a parameter preprocessing grouping multiphysical multi-objective high-dimensional multilevel optimization method. Taking M2 as an example, a global optimization design was carried out for the high-dimensional structural parameters brought by PMS, which improved the electromagnetic performance of M2 and reduced the vibration noise and rotor stress.

Continuously growing demand for natural products with pharmacological activities has promoted the development of microbial transformation techniques, thereby facilitating the efficient production of natural products and the mining of new active compounds. Furthermore, due to the shortcomings and defects of microbial transformation, it is an important scientific issue of social and economic value to improve and optimize microbial transformation technology in increasing the yield and activity of transformed products. In this review, the aspects regarding the optimization of fermentation and the cross-disciplinary strategy, leading to the microbial transformation of increased levels of the high-efficiency process from natural products of a plant or microbial origin, were discussed. Additionally, due to the increasing craving for targeted and efficient methods for detecting transformed metabolites, analytical methods based on multiomics were also discussed. Such strategies can be well exploited and applied to the production of more efficient and more natural products from microbial resources.

This study investigates the integrated scheduling of production and distribution within a time-sensitive supply chain at the operational level. In addition, the study specifically focuses on parallel machine scheduling and vehicle routing problem with time windows (VRP-TW) while considering flexible departure times. A hybrid multi-level optimization (HMLO) framework is developed, decomposing the problem into two primary phases: parallel machine scheduling and distribution scheduling. The initial phase entails the establishment of a comprehensive production schedule, whereas the subsequent phase focuses on segmenting the orders into batches and developing a complete distribution schedule. The framework incorporates the ant colony system (ACS) within the HMLO structure to optimize distribution costs. This is accomplished with both metaheuristics and heuristics to determine the optimal values for the decision variables. Extensive numerical experiments demonstrate that the proposed framework demonstrate that the suggested framework can yield optimal solutions for small-scale instances. Furthermore, it outperforms existing methods, including those utilized in LINGO software, for medium and large-scale instances regarding both convergence and solution quality. For large-scale instances, the proposed method achieves an average improvement of 5 to 30% when compared to LINGO solutions.

Hub-and-spoke networks play a critical role in reducing cost and enhancing service levels in various infrastructural sectors since hubs act as the consolidation and transshipment points of the flows. The failure of hubs in such a network can cause severe disruptions. While disruptions can be natural or man-made, a disruption by a rational individual or entity can be significantly detrimental to the network and is often studied as an interdiction problem. It is important to take interdiction effects at the design stage; therefore, we study the three-level capacitated hub-and-spoke network design problem from the perspective of a defender who considers the risk of interdiction by a rational attacker. Within the three levels, the upper level represents the network design level, and the lower two levels represent the bi-level hub interdiction problem. The introduction of capacity constraints within an interdiction model dramatically increases the complexity of the problem, as there can be some unfulfilled flows post-interdiction. Moreover, a flow may or may not be fulfilled through the least-cost route using the nearest hubs. This work makes two major contributions: the first contribution is on the efficient handling of the bi-level hub interdiction problem using the Dual-based approach and the Penalty-based approach, and the second contribution is on solving the overall three-level problem using a super valid inequality. These two contributions allow us to solve large-scale versions of the capacitated multiple allocation p-median hub location problem under the risk of interdiction, which is otherwise mathematically intractable and can be handled only using complete enumeration techniques.

We deal here with job scheduling under the constraint of encapsulated renewable and non-renewable resources. For the sake of understanding, we rely here on a case study related to energy production by a photovoltaic platform. This context means synchronizing production and consumption in order to both minimize production cost and achieve the jobs according to specific purposes. Because of both the complexity of resulting bi-level model and the fact that synchronization most often involves distinct players with their own agenda and non shared information, we shortcut the production level with the help of surrogate estimators. Those estimators involve flexible pricing mechanisms and machine learning devices. According to this, we first perform a structural analysis of our model, before designing and testing several algorithms that implement this surrogate based approach.

Modelling real processes often results in several suitable models. In order to be able to distinguish, or discriminate, which model best represents a phenomenon, one is interested, e.g., in so-called T-optimal designs. These consist of the (design) points from a generally continuous design space at which the models deviate most from each other under the condition that they are best fitted to those points. Thus, the T-criterion represents a bi-level optimization problem, which can be transferred into a semi-infinite one but whose solution is very unstable or time consuming for non-linear models and non-convex lower- and upper-level problems. If one considers only a finite number of possible design points, a numerically well tractable linear semi-infinite optimization problem arises. Since this is only an approximation of the original model discrimination problem, we propose an algorithm which alternately and adaptively refines discretizations of the parameter as well as of the design space and, thus, solves a sequence of linear semi-infinite programs. We prove convergence of our method and its subroutine and show on the basis of discrimination tasks from process engineering that our approach is stable and can outperform the known methods.

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.

Engineering bead-on-string architectures with refined interfacial interactions and low ion diffusion barriers is a highly promising but challenging approach for lithium/sodium storage. Herein, a spindle-chain-structured Fe-based metal organic frameworks (MIL-88A) self-sacrificial template was constructed via the seed-mediated growth of Fe 3+ and fumaric acid in an aqueous solution, which is an environmentally friendly synthesis route. The seed-mediated growth method effectively segregates the nucleation stage from the subsequent growth phase, offering precise control over the growth patterns of MIL-88A through manipulation of kinetic and thermodynamic parameters. The structural diversity, fast ion/electron diffusion, and unique interfaces of whole anodes are simultaneously enhanced through optimization of the spindle-chain structure of Fe 2 O 3 @N-doped carbon nanofibers (FO@NCNFs) at the atomic, nano, and macroscopic levels. Benefiting from their heteroatom-doping conductive networks, porous structure, and synergistic effects, FO@NCNFs exhibit a remarkable rate performance of 167 mAh g −1 at 10 A g −1 after 2000 cycles for lithium-ion batteries (LIBs) and long-term cycling stability with a sustained capacity of 260 mAh g −1 at 2 A g −1 after 2000 cycles for sodium-ion batteries (SIBs). This versatile approach for fabricating bead-on-string architectures at both the nanoscale and macroscale is promising for the development of high-energy–density and high-power-density electrode materials. Graphical Abstract

In this paper, a fuzzy hierarchical optimization framework is proposed for identifying potential antiviral targets for treating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the heart. The proposed framework comprises four objectives for evaluating the elimination of viral biomass growth and the minimization of side effects during treatment. In the application of the framework, Dulbecco’s modified eagle medium (DMEM) and Ham’s medium were used as uptake nutrients on an antiviral target discovery platform. The prediction results from the framework reveal that most of the antiviral enzymes in the aforementioned media are involved in fatty acid metabolism and amino acid metabolism. However, six enzymes involved in cholesterol biosynthesis in Ham’s medium and three enzymes involved in glycolysis in DMEM are unable to eliminate the growth of the SARS-CoV-2 biomass. Three enzymes involved in glycolysis, namely BPGM, GAPDH, and ENO1, in DMEM combine with the supplemental uptake of L-cysteine to increase the cell viability grade and metabolic deviation grade. Moreover, six enzymes involved in cholesterol biosynthesis reduce and fail to reduce viral biomass growth in a culture medium if a cholesterol uptake reaction does not occur and occurs in this medium, respectively.