The design freedom enabled by additive manufacturing, combined with the unique properties of generative lattice structures, has gained increasing attention in thermal system technologies, especially for the development and fabrication of heat exchangers. In this study, a hybrid heat exchanger was developed by integrating a Diamond unit cell, known for its high thermal performance, with an IWP unit cell, which offers low flow resistance. The goal is to reduce flow resistance while maintaining heat transfer efficiency. A physical model was simulated using ANSYS software to evaluate the heat exchanger’s performance. The results indicate that integrating IWP cells into a Diamond unit cell structure effectively reduces flow resistance, though with a slight compromise in heat transfer performance. This balance provides a viable design strategy for heat exchangers in applications requiring precise flow resistance control.

Additive manufacturing processes and generative lattice structure are two increasingly popular methods in thermal management applications, particularly in the design and production of heat sinks for electronic devices. In this paper additive manufacturing was used to create heat sinks with lattice structures, specifically triple periodic minimum surfaces (TPMS). Where a conventional pin-fin heat conductor was compared with a heat sink using TPMS lattices, including Gyroid, IWP, and Neovius structures, by evaluating thermal performance. A model was physically simulated to study the thermal performance of the dispersants on the ANSYS software. The results show that the heat sink incorporating TPMS lattices exhibits superior thermal performance than the traditional pin heat sink. The research demonstrates the potential of additive manufacturing to create complex geometries that can improve the thermal dissipation of electronic devices.

Classifying and modeling texture images, especially those with significant rotation, illumination, scale, and view-point variations, is a hot topic in the computer vision field. Inspired by local graph structure (LGS), local ternary patterns (LTP), and their variants, this paper proposes a novel image feature descriptor for texture and material classification, which we call Petersen Graph Multi-Orientation based Multi-Scale Ternary Pattern (PGMO-MSTP). PGMO-MSTP is a histogram representation that efficiently encodes the joint information within an image across feature and scale spaces, exploiting the concepts of both LTP-like and LGS-like descriptors, in order to overcome the shortcomings of these approaches. We first designed two single-scale horizontal and vertical Petersen Graph-based Ternary Pattern descriptors ($PGTP_{h}$and$PGTP_{v}$). The essence of$PGTP_{h}$and$PGTP_{v}$is to encode each$5\times 5$image patch, extending the ideas of the LTP and LGS concepts, according to relationships between pixels sampled in a variety of spatial arrangements (i.e., up, down, left, and right) of Petersen graph-shaped oriented sampling structures. The histograms obtained from the single-scale descriptors$PGTP_{h}$and$PGTP_{v}$are then combined, in order to build the effective multi-scale PGMO-MSTP model. Extensive experiments are conducted on sixteen challenging texture data sets, demonstrating that PGMO-MSTP can outperform state-of-the-art handcrafted texture descriptors and deep learning-based feature extraction approaches. Moreover, a statistical comparison based on the Wilcoxon signed rank test demonstrates that PGMO-MSTP performed the best over all tested data sets.

This paper presents an efficient handcrafted texture operator for texture modeling and classification. The proposed descriptor, referred to as local directional decoded ternary pattern (LDDTP), consists in encoding both directional pattern features and contrast information in a compact way based on local derivative variations. The proposed operator first computes for each pixel within its 3×3 overlapping square neighborhood, on the one hand, central edge response through the 2ndderivative of Gaussian filter, and on the other hand, eight directional edge responses using the eight Frei-Chen masks to capture more detailed information. Then, spatial relationships among the neighboring pixels through the generated edge responses are exploited independently with the help of the concepts of LTP and LDP operators to enhance the discriminative power. Finally, the produced LDDTP pattern is splitted into two distinct parts: local directional decoded ternary pattern lower ( LDDTPL) and local directional decoded ternary pattern upper ( LDDTPU), which are combined into hybrid distributions to form the final LDDTP feature descriptor. Experimental results on eight publicly available texture datasets showed that the proposed LDDTP descriptor achieves classification performance, which is competitive or better than several old and recent state-of-the-art LBP variants.