Lattice structures emerged as a revolutionary class of materials with significant applications in aerospace, biomedical engineering, and mechanical design due to their exceptional strength-to-weight ratio, energy absorption properties, and structural efficiency. This review systematically examines recent advancements in lattice structures, with a focus on their classification, mechanical behavior, and optimization methodologies. Stress distribution, deformation capacity, energy absorption, and computational modeling challenges are critically analyzed, highlighting the impact of manufacturing defects on structural integrity. The review explores the latest progress in hybrid additive manufacturing, hierarchical lattice structures, modeling and simulation, and smart adaptive materials, emphasizing their potential for self-healing and real-time monitoring applications. Furthermore, key research gaps are identified, including the need for improved predictive computational models using artificial intelligence, scalable manufacturing techniques, and multi-functional lattice systems integrating thermal, acoustic, and impact resistance properties. Future directions emphasize cost-effective material development, sustainability considerations, and enhanced experimental validation across multiple length scales. This work provides a comprehensive foundation for future research aimed at optimizing biomimetic lattice structures for enhanced mechanical performance, scalability, and industrial applicability.

Hydrogen is a promising future fuel to enable the transition of transportation sector toward carbon neutrality. The direct utilization of H₂ in internal combustion engines (ICEs) faces three major challenges: high NO_x emissions, severe pressure rise rates, and pre-ignition at mid to high loads. In this study, the potential of H₂ combustion in a truck-size engine operated in spark ignition (SI) and pre-chamber (PC) mode was investigated. To mitigate the high pressure rise rate with the SI configuration, the effects of three primary parameters on the engine combustion performance and NO_x emissions were evaluated, including the compression ratio (CR), the air–fuel ratio, and the spark timing. In the simulations, the severity of the pressure rise was evaluated based on the maximum pressure rise rate (MPRR). Lower compression ratios were assessed as a means to mitigate the auto-ignition while enabling a wider range of engine operation. The study showed that by lowering CR from 16.5:1 to 12.5:1, an indicated thermal efficiency of 47.5% can be achieved at 9.4 bar indicated mean effective pressure (IMEP) conditions. Aiming to restrain the auto-ignition while maintaining good efficiency, growth in λ was examined under different CRs. The simulated data suggested that higher CRs require a higher λ, and due to practical limitations of the boosting system, λ at 4.0 was set as the limit. At a fixed spark timing, using a CR of 13.5 combined with λ at 3.33 resulted in an indicated thermal efficiency of 48.6%. It was found that under such lean conditions, the exhaust losses were high. Thus, advancing the spark time was assessed as a possible solution. The results demonstrated the advantages of advancing the spark time where an indicated thermal efficiency exceeding 50% was achieved while maintaining a very low NO_x level. Finally, the optimized case in the SI mode was used to investigate the effect of using the PC. For the current design of the PC, the results indicated that even though the mixture is lean, the flame speed of H₂ is sufficiently high to burn the lean charge without using a PC. In addition, the PC design used in the current work induced a high MPRR inside the PC and MC, leading to an increased tendency to engine knock. The operation with PC also increased the heat transfer losses in the MC, leading to lower thermal efficiency compared to the SI mode. Consequently, the PC combustion mode needs further optimizations to be employed in hydrogen engine applications.

Improving photovoltaic (PV) panel performance under extreme climatic conditions is critical for advancing sustainable energy systems. In hyper-arid regions, elevated operating temperatures significantly reduce panel efficiency. This study investigates and compares three cooling techniques—air cooling, water cooling, and porous media cooling—using thermal and electrical modeling based on CFD simulations in ANSYS. The numerical model replicates a PV system operating under peak solar irradiance (900 W/m²) and realistic ambient conditions in Adrar, Algeria. Simulation results show that air cooling leads to a modest temperature reduction of 6 °C and a marginal efficiency gain of 0.25%. Water cooling, employing a top-down laminar flow, reduces cell temperature by over 35 °C and improves net electrical output by 30.9%, despite pump energy consumption. Porous media cooling, leveraging passive evaporation through gravel, decreases panel temperature by around 30 °C and achieves a net output gain of 26.3%. Mesh sensitivity and validation against experimental data support the accuracy of the model. These findings highlight the significant potential of water and porous material cooling strategies to enhance PV performance in hyper-arid environments. The study also demonstrates that porous media can deliver high thermal effectiveness with minimal energy input, making it a suitable low-cost option for off-grid applications. Future work will integrate long-term climate data, real diffuser geometries, and experimental validation to further refine these models.

Biomimetic lattice structures, inspired by natural architectures such as bone, coral, mollusk shells, and Euplectella aspergillum, have gained increasing attention for their exceptional strength-to-weight ratios, energy absorption, and deformation control. These properties make them ideal for advanced engineering applications in aerospace, biomedical devices, and structural impact protection. This study presents a comprehensive bibliometric analysis of global research on biomimetic lattice structures published between 2020 and 2025, aiming to identify thematic trends, collaboration patterns, and underexplored areas. A curated dataset of 3685 publications was extracted from databases like PubMed, Dimensions, Scopus, IEEE, Google Scholar, and Science Direct and merged together. After the removal of duplication and cleaning, about 2226 full research articles selected for the bibliometric analysis excluding review works, conference papers, book chapters, and notes using Cite space, VOS viewer version 1.6.20, and Bibliometrix R packages (4.5. 64-bit) for mapping co-authorship networks, institutional affiliations, keyword co-occurrence, and citation relationships. A significant increase in the number of publications was found over the past year, reflecting growing interest in this area. The results identify China as the most prolific contributor, with substantial institutional support and active collaboration networks, especially with European research groups. Key research focuses include additive manufacturing, finite element modeling, machine learning-based design optimization, and the performance evaluation of bioinspired geometries. Notably, the integration of artificial intelligence into structural modeling is accelerating a shift toward data-driven design frameworks. However, gaps remain in geometric modeling standardization, fatigue behavior analysis, and the real-world validation of lattice structures under complex loading conditions. This study provides a strategic overview of current research directions and offers guidance for future interdisciplinary exploration. The insights are intended to support researchers and practitioners in advancing next-generation biomimetic materials with superior mechanical performance and application-specific adaptability.