Ahsanullah University of Science and Technology

A high-performance permanent magnet linear generator should have low mover mass, sufficient output power, high efficiency, and high power density. Simultaneously improving the multiple parameters of a linear generator is quite challenging due to conflicting parametric requirements. This paper proposes an advanced design optimization procedure combining particle swarm optimization and genetic algorithm to address conflicting requirements. A problem statement is presented regarding such requirements through mathematical model analysis. Simulation work is executed using a finite element-based platform, ANSYS/Maxwell. Particle swarm optimization is first applied to identify an effective stator configuration, where the generator with E 1 stator supplies 3.45 kW more power than the E 2 counterpart. On the contrary, despite a 36.46 % reduction in the mover mass of C-stator, its efficiency is 8.96 % higher than that of the linear generator with the E 1 stator. A genetic algorithm is then applied to optimize the initial C-cored design, yielding simultaneous enhancements of 7.12 % in power, 13.97 % in power density, and 3.03 % in efficiency, along with a 32.5 % reduction in mover mass. The workflow and outcomes are summarized through a structured optimization flowchart. • Identification of conflicting parameters through mathematical model analysis. • Proposal of an advanced methodology to improve multiple parameters simultaneously. • Improvement of multiple parameters concurrently despite reducing the mover mass.

This study presents the computational fluid dynamics (CFD) driven design and optimization of an intake manifold for a high-performance, restrictor-limited single-cylinder engine. The primary objective was to minimize pressure loss and maximize airflow velocity under the stringent constraint of a 20 mm inlet restrictor. SolidWorks was used for modeling, and ANSYS Fluent was used for simulation. The designs were tested at mass flow rates of 0.0732, 0.0852, and 0.0952 kg/s. The results showed that Design Model 5 performed better than the others, achieving airflow velocities of 220.6, 251.4, and 281.2 m/s, which is 4 to 9% higher than competing models. It also reduced pressure drop by over 69% lower than the other design models, measuring 572.02, 725.49, and 862.48 Pa for the respective mass flow rates. Mesh independence was confirmed with a 5 mm element size, and validation against previous studies indicated less than 0.2% deviation in pressure and velocity predictions. The optimized design’s strong performance stems from smooth airflow with minimal vortex formation demonstrates a significant enhancement in volumetric efficiency for restrictor-limited engines. This study provides a validated CFD framework for designing high-efficiency intake systems under strict flow constraints.

Poetry summarization presents unique challenges for Large Language Models due to its dense use of metaphor, imagery, rhythm, and stylistic elements. This study analyzes the zero-shot techniques for poem summarization using the five state-of-the-art Large Language Models (LLMs), such as Gemini 2.5 Flash, Groq Mini, DistilBART CNN-12-6, LLaMA 3.3 70B Versatile, and Moonshot AI Kimi K2 Instruct. This study also evaluated the summaries using ROUGE-1, ROUGE-2, ROUGEL, and BERTScore to measure both lexical overlap and semantic similarity. After that, use a novel LLM-as-a-Judge approach where evaluate summaries on content similarity, quality, completeness, accuracy, and overall match, and average score for each of the LLMs. Experimental results show that DistilBART CNN12-6 achieves the highest ROUGE and BERTScore performance, while Gemini 2.5 Flash achieves the best performance in LLM-as-a-judge evaluation. This study provides a clear and scalable way to evaluate poem summarization. Overall, this study shows that zero-shot techniques in LLMs can effectively generate high-quality poem summaries.

This research optimized the effects of air-gap length and load resistance on the electromagnetic performance of the free-piston linear generator (FPLG). ANSYS Maxwell was used to build a finite element model, and dynamic optimization was performed by changing the air gap and load resistance while keeping the translator’s velocity constant. To improve magnetic coupling and reduce losses, the proposed design included M530-50A magnetic cores and a neodymium-iron-boron N48H grade permanent magnet. At an optimized air gap of 1.5 mm and a load resistance of 60 Ω, the proposed generator reduced force ripple by 32.9% compared to the initial generator, as depicted in the simulation result. Magnetic flux linkage, RMS output power, and peak output voltage of the proposed optimized generator increased by 6%, 2.9%, and 11.5%, respectively. The findings showed that determining the optimal air-gap significantly improved the FPLG’s overall performance and conversion efficiency.