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This study aims to solve the problem that under light-load conditions, the output voltage regulation capability is lost due to the fact that the voltage gain of the LLC resonant converter does not decrease with the increase of the switching frequency.

In this paper, the impedance model considering the parasitic parameters of the primary and secondary sides is calculated under light-load conditions, the limitations of the previous method are explained and a new circuit improvement is proposed.

In this paper, an improved circuit is proposed, and the impedance Bode plot is used to verify that the circuit can effectively improve the voltage gain problem under light-load conditions. Finally, the experimental results verify the effectiveness of the proposed circuit through comparison with traditional solutions and circuits.

In this paper, the impedance model considering the parasitic parameters of the primary and secondary sides is calculated, the limitations of the previous method are explained and a new circuit improvement is proposed. When compared with the previous method, the proposed circuit improvement can suppress the voltage gain increase that occurs when the switching frequency increases to a certain level.

This paper aims to better design the resonant tank parameters for LLC resonant converter. And, it is found that under heavy load, the voltage gain is affected by junction capacitors of the primary side switching and the parasitic parameters of the secondary side diodes converted to the primary side, which will cause the voltage gain decreased when the switching frequency decreased.

This paper proposes an optimization parameters design method to solve this problem, which was based on impedance model considering the parasitic parameters of switching devices and diodes.

The effectiveness of the proposed method is verified by impedance Bode plots and experimental results.

From the perspective of impedance modeling, this paper finds the reasons for the insufficient voltage regulation capability of LLC resonant converters under heavy load and finds solutions through analysis.

This study aims to explore the evolving field of physics-informed neural networks (PINNs) through an analysis of 996 records retrieved from the Web of Science (WoS) database from 2019 to 2022.

WoS database was analyzed for PINNs using an inhouse python code. The author’s collaborations, most contributing institutes, countries and journals were identified. The trends and application categories were also analyzed.

The papers were classified into seven key domains: Fluid Dynamics and computational fluid dynamics (CFD); Mechanics and Material Science; Electromagnetism and Wave Propagation; Biomedical Engineering and Biophysics; Quantum Mechanics and Physics; Renewable Energy and Power Systems; and Astrophysics and Cosmology. Fluid Dynamics and CFD emerged as the primary focus, accounting for 69.3% of total publications and witnessing exponential growth from 22 papers in 2019 to 366 in 2022. Mechanics and Material Science followed, with an impressive growth trajectory from 3 to 65 papers within the same period. The study also underscored the rising interest in PINNs across diverse fields such as Biomedical Engineering and Biophysics, and Renewable Energy and Power Systems. Furthermore, the focus of the most active countries within each application category was examined, revealing, for instance, the USA’s significant contribution to Fluid Dynamics and CFD with 319 papers and to Mechanics and Material Science with 66 papers.

This analysis illuminates the rapidly expanding role of PINNs in tackling complex scientific problems and highlights its potential for future research across diverse domains.

In recent years, additive manufacturing (AM) has started to be used for manufacturing real functional parts and assemblies for critical applications in aerospace, automotive, and machinery industries. Most complex or assembled parts require internal features (IF) such as holes, channels, slots, or guides for locational and mating requirements. Therefore, it is critical to understand and compare the structural and mechanical properties of additively manufactured and conventionally machined IFs.

In this study, mechanical and microstructural properties of Inconel 718 (Inc718) alloy internal features, manufactured either as-built with AM or machining of additively manufactured (AMed) part thereafter were investigated.

The results showed that the average ultimate tensile strength (UTS) of additively manufactured center internal feature (AM-IF) is almost analogous to the machined internal feature (M-IF). However, the yield strength of M-IF is greater than that of AM-IF due the greater surface roughness of the internal feature in AM-IF, which is deemed to surpass the effect of microstructure on the mechanical performance. The results of digital image correlation (DIC) analysis suggest that AM-IF and M-IF conditions have similar strain values under the same stress levels but the specimens with as built IF have a more locally ductile region around their IF, which is confirmed by hardness test results. But this does not change global elongation behavior. The microstructural evolution starting from as-built (AB) and heat-treated (HT) samples to specimens with IF are examined. The microstructure of HT specimens has bimodal grain structure with d phase while the AB specimens display a very fine dendritic microstructure with the presence of carbides. Although they both have close values, machined specimens have a higher frequency of finer grains based on SEM images.

It was shown that the concurrent creation of the IF during AM can provide a final part with a preserved ultimate tensile strength and elongation but a decreased yield strength. The variation in UTS of AM-IF increases due to the surface roughness near the internal feature as compared to smooth internal surfaces in M-IF. Hence, the outcomes of this study are believed to be valuable for the industry in terms of determining the appropriate production strategy of parts with IF using AM and postprocessing processes.