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In order to reduce magnetic components for a current-fed dual active bridge converter, this paper proposes a dual coupled-inductor (DCI) structure, which integrates two DC inductors, one high-frequency transformer, and one leakage inductor into two EE cores. By analyzing the principle of the magnetic components, a derivation process is presented to modify the current-fed dual active bridge converter. To simplify the design and enhance efficiency, an equal air gap length optimization method is proposed. And the geometric parameters with the highest efficiency are optimized based on losses. Finally, the feasibility and effectiveness of the above design were verified through a 1 kW test prototype.

In order to improve the performance of a transonic centrifugal compressor stage, non-axisymmetric endwall profiling optimization was conducted for the diffuser under design condition, Artificial Neural Network (ANN) and Genetic Algorithm (GA) were used to execute the optimization with the objective of maximizing the isentropic efficiency of the compressor stage. The influence mechanism of non-axisymmetric endwall profiling on flow field and performance was discussed. Results show non-axisymmetric endwall profiling is an effective way to significantly reduce the flow loss in the diffuser. The total pressure loss of the diffuser decreases by 9.31% and 20.29% for NA0.70 and NA1.40 respectively. The profiled endwall suppresses the flow separation through accelerating the low-energy flow and reducing lateral pressure gradient. The corresponding high vorticity within the flow separation zone is reduced, which delays the formation and development of the flow separation. The diffuser becomes more fore-loaded, the overall blade loading is not affected, and the pressure ratio of the compressor stage is improved as well. At the outlet of the diffuser, the more uniform flow angle and much lower total pressure loss along spanwise are obtained. However, the backflow with high velocity gathering near the shroud of the diffuser makes the mass flow rate decrease and easily induce the stall, which results in the smaller operating range for both profiled endwall.

With higher operation temperature required by the advanced aero-turbine, the conventional single web turbine disk (SWD) has reached its limits. At this circumstance, a twin-web disk (TWD) has been proposed as a breakthrough by J Eng Gas Turbines Power Trans ASME 124:298–302, (2002) for its improvements in heat transfer, structural strength and weight loss. However, this novel structure needs new cooling process which brings problems with pressure loss. Fins are designed in this paper in order to increase the outlet pressure and enhance the heat transfer, at the same time demonstrated by the computational fluid dynamics (CFD) analysis. Then, the multidisciplinary design of optimization (MDO) has been performed to find a proper shape and layout of the fins with the minimum stress and maximum outlet pressure. A Kriging surrogate model is also used to accelerate the optimization pace. Because it is a typical multi-objective optimization problem (MOP), the Pareto Front set is obtained in this paper. The results show that the TWD with fins exhibits a better performance in heat transfer and outlet pressure than the one without fins. This structure would be a future trend in TWD design.

Since the early 1920s, the design of mufflers has become an influential topic of study among engineers, as they have the ability to reduce noise from industrial machinery, combustion engines, refrigerators, etc. However, since its applications are strongly dependent on the target frequencies and the adopted geometries, efficient muffler design methods are still under investigation up to this day. With that in mind, this paper presents a multi-domain acoustic topology optimization methodology applied to the design of reactive and dissipative expansion chamber mufflers. Based on the Bi-directional Evolutionary Structural Optimization (BESO) algorithm, the proposed approach also uses a novel material interpolation scheme that considers acoustic, porous and rigid domains during the optimization process, hence configuring a multi-phase procedure. The simulation of porous materials is performed by the Johnson–Champoux–Allard (JCA) mathematical formulations, while the numerical solution is obtained by the finite element method. To further compose the study, the objective function is defined as the mean value of the sound Transmission Losses (TL) obtained along one, two or three different frequency bands, while the proposed multi-domain BESO (mdBESO) algorithm is applied to the design of single and multi-chamber mufflers. Here, more than one muffler per BESO iteration is considered, being also possible to optimize for specific frequency bands in predefined chambers. The effectiveness of both, the novel material interpolation scheme and the mdBESO algorithm, are highlighted, showing considerable TL enhancements in the broad range of frequencies chosen, while also presenting clear optimized partitions as result.

As we face issues of fossil fuel depletion and environmental pollution, it is becoming increasingly important to transition towards clean renewable energies and electric vehicles (EVs). However, designing electric motors with high power density for EVs can be challenging due to space and weight constraints, as well as issues related to power loss and temperature rise. In order to overcome these challenges, a significant amount of research has been conducted on designing high-power-density electric motors with advanced materials, improved physical and mathematical modeling of materials and the motor system, and system-level multidisciplinary optimization of the entire drive system. These technologies aim to achieve high reliability and optimal performance at the system level. This paper provides an overview of the key technologies for designing high-power-density electric motors for EVs with high reliability and system-level optimal performance, with the focus on advanced magnetic materials and the proper modeling of core losses under two-dimensional or three-dimensional vectorial magnetizations. This paper will also discuss the major challenges associated with designing these motors and the possible future research directions in the field.

In recent years, the optimization of diesel engine exhaust mufflers has predominantly targeted acoustic performance, while the impact on engine power performance has often been overlooked. Therefore, this paper proposes a parallel perforated tube expansion muffler and conducts a numerical analysis of its acoustic and aerodynamic performance using the finite element method. Then, a Kriging model is established based on the Design of Experiments to reveal the impact of different parameter couplings on muffler performance. With transmission loss (TL) and pressure loss (PL) as the optimization objectives, a multi-objective optimization study is carried out using the competitive multi-objective particle swarm optimization (CMOPSO). The optimization results indicate that this method can simplify the optimization model and improve optimization efficiency. After CMOPSO calculation, the average TL of the muffler increased from 27.3 dB to 31.6 dB, and the PL decreased from 1087 Pa to 953 Pa, which reduced the exhaust noise and improved the fuel economy of the engine, thus enhancing the overall performance of the muffler. This work provides a reference and guidance for the optimal design of mufflers for small agricultural diesel engines.

This study proposes a composite acoustic porous duct metamaterial muffler composed of a perforated tortuous channel and an externally wrapped porous layer, integrating both structural resonance and material damping effects. Theoretical models for the perforated plate, tortuous channel, and porous material were established, and analytical formulas for the total acoustic impedance and transmission loss of the composite structure were derived. Finite element simulations verified the accuracy of the models. A systematic parametric study was then performed on the effects of porous material type, thickness, and width on acoustic performance, showing that polyester fiber achieves the best results at a thickness of 30 mm and a width of 5 mm. Further analysis of periodic distribution modes revealed that axial periodic arrangement significantly enhances the peak noise attenuation, radial periodic arrangement broadens the effective bandwidth, and multi-frequency parallel configurations further expand the operating range. Considering practical duct conditions, a single-layer multi-cell array was constructed, and its modal excitation mechanism was clarified. By employing the Particle Swarm Optimization (PSO) algorithm for multi-parameter optimization, the average transmission loss was improved from 26.493 dB to 29.686 dB, corresponding to an increase of approximately 12.05%. Finally, physical samples were fabricated via 3D printing, and four-sensor impedance tube experiments confirmed good agreement among theoretical, numerical, and experimental results. The composite structure exhibited an average experimental transmission loss of 24.599 dB, outperforming the configuration without porous material. Overall, this work highlights substantial scientific and practical advances in sound energy dissipation mechanisms, structural optimization design, and engineering applicability, providing an effective approach for broadband and high-efficiency duct noise reduction.

This paper considers the problem of how to construct the optimal multi-period portfolio for investors with loss aversion in fuzzy environment. Firstly, we regard the return rates of the risky assets as fuzzy numbers and use the value function in prospect theory to transform the return rate of a portfolio into perceived value, which can reflect investors’ loss aversion. Moreover, due to the fact that investors’ perception level toward risk may vary with the loss aversion degree, we propose a new risk measure based on the perceived value. Then, we formulate the objectives of maximizing the cumulative expected perceived value and minimizing the cumulative perceived risk and propose a multi-period portfolio selection model with diversification constraint. Furthermore, to solve the proposed model, we design a multiple particle swarm optimization algorithm with respect to its specific situation. Finally, using the data from real financial market, we construct a real case to illustrate the effectiveness of the model and algorithm. The results show that loss aversion has an important effect on investors’ investment decisions, and the proposed model could provide more reasonable strategies for investors with different loss aversion degrees.

The inverted bucket steam valve is a key piece of equipment in steam systems. Optimizing its noise reduction performance via intelligent algorithms is crucial for enhancing the stability of steam systems. In this study, the nutcracker optimization algorithm (NOA) was investigated and improved. A simulation method coupling computational fluid dynamics (CFD) with acoustic software was employed to characterize the acoustic properties of inverted bucket steam valves equipped with noise-reducing elements of different structures. Subsequently, the structural dimensions of the valve’s noise-reducing element were optimized using a support vector machine (SVM)-based surrogate model and the improved NOA. Concurrently, experimental tests were conducted on the inverted bucket steam valve before and after optimization to validate the simulation accuracy. The experimental results demonstrate that the SVM-NOA increases the maximum transmission loss (TL) of the valve’s noise-reducing element by 44.14 dB, with the error between experimental and simulation results being less than 3%. This verifies the accuracy of the acoustic simulation method and confirms the practicality and versatility of the SVM-NOA for solving real-world engineering problems.