A low-profile and dual-wideband circularly polarized (CP) shared aperture antenna is demonstrated for satellite-assisted Internet of Things (SATA-IoT) communications. It comprises an X-band antenna and a$4\times 4$Ka-band antenna array, both magnetoelectric (ME) dipole structures with complementary structures, and only have$0.08{\lambda }_{\mathrm { low}}$thickness. Four mesh-structure dipole arms are utilized for the X-band antenna to integrate the X/Ka band antenna into the limited aperture while maintaining good isolation. Meanwhile, stubs and a sequential rotation feeding (SRF) network are adopted to improve the axial ratio (AR) performance of the two antennas, respectively. The measured results indicate that the antenna achieves an overlapped bandwidth ($|S_{11}|\lt -10$dB, AR ¡ 3) of 10.8% (8.7–9.7 GHz) and 29.32% (25.73–34.57 GHz), with isolations greater than 20 dB in both bands. The measured peak realized gains are 8.9 and 15 dBi, respectively. The proposed antenna realized exceptional low-profile and dual-wideband CP performance, making it a promising candidate for SATA-IoT communications.

Emerging additive manufacturing (AM) technologies, specifically additively manufactured electronics (AME), 4-D printing, and origami, are reshaping the design capabilities and functionalities of contemporary electronic devices. Cutting-edge 3-D/4-D printing technologies facilitate the prototyping and realization of complex electronic functions that are challenging to conventional methods. This article provides a comprehensive overview of the evolving techniques in AME, 4-D printing, and origami, employing multimaterials (conductive and dielectric materials) and shape-memory materials (SMMs) to fabricate functional electronic components and devices. Additionally, the overview delves into the state-of-the-art AME and 4-D-printed electronic components across diverse fields, including biomedical electronics, space engineering, and the advancements in the next-generation wireless communications and sensing.

The Lattice Boltzmann Method (LBM) is a class of Computational Fluid Dynamics methods which models the fluid as fictive particles. In this paper, we report our work on SunwayLB, which enables LBM based solutions aiming for industrial applications using advanced heterogeneous systems such as the Sunway supercomputers. We propose several techniques to boost the simulation speed and improve the scalability of SunwayLB, including a customized multi-level domain decomposition and data sharing scheme, a carefully orchestrated strategy to fuse kernels with different performance constraints for a more balanced workload, and optimization strategies for assembly code. Based on these optimization schemes, we manage to scale SunwayLB on three advanced supercomputers: Sunway TaihuLight, the new Sunway Supercomputer and a GPU cluster. On Sunway TaihuLight, our largest simulation involves up to 5.6 trillion lattice cells, achieving 11,245 billion cell updates per second (GLUPS), 77% memory bandwidth utilization and a sustained performance of 4.7 PFlops. We further improve the memory bandwidth utilization and computational efficiency using the unique features of a new generation of Sunway supercomputer. On the new Sunway Supercomputer, the largest simulation contains over 4.2 trillion lattice cells, resulting in 6,583 GLUPS, 81% memory bandwidth utilization and a sustained performance of 2.76 PFlops. To evaluate the portability of our code, we also adapt our code to a GPU cluster with tailored optimization techniques, resulting in 191x speedup and 83.8% memory bandwidth utilization. We demonstrate a series of computational experiments for extreme-large scale fluid flow, as examples of real-world applications, to check the validity and performance of our work. The results show that our implementation is competent to be a highly scalable and efficient solution for large-scale CFD problems on heterogeneous systems.

The Lattice Boltzmann Method (LBM) has gained widespread popularity due to its applicability in fluid dynamics, chemical engineering, material science, and other domains. In this work, we present an optimized implementation of the LBM, with a specific focus on achieving superior performance and scalability on advanced heterogeneous systems such as the new Sunway supercomputer. To accomplish this, we employ several techniques, including kernel fusion to enhance temporal and spatial locality, a customized multi-level domain decomposition and data sharing scheme, and pipelining strategies that are tailored to the SW26010-Pro processor. As a result of these optimizations, we have successfully scaled our code to a total of 39,000,000 CPU cores. Our largest simulation, which encompassed over 42 trillion lattice cells, achieved an impressive 67,018 billion lattice cell updates per second (GLUPS), with 82.9% memory bandwidth utilization, and a sustained performance of 28 PFlops. In order to assess the portability of our implementation, we also adapted our code to run on a GPU cluster, utilizing a range of tailored optimization techniques. Our results demonstrated a 191x speedup, along with 83.8% memory bandwidth utilization. Our proposed approach marks a significant milestone in the field of LBM implementations, as it demonstrates unprecedented scalability by effectively utilizing over 39,000,000 cores while maintaining exceptional parallel efficiency and computational performance. This achievement establishes our method as a compelling solution for addressing large-scale computational fluid dynamics challenges on heterogeneous systems.

A state-of-the-art large eddy simulation code has been developed to solve compressible flows in turbomachinery. The code has been engineered with a high degree of scalability, enabling it to effectively leverage the many-core architecture of the new Sunway system. A consistent performance of 115.8 DP-PFLOPs has been achieved on a high-pressure turbine cascade consisting of over 1.69 billion mesh elements and 865 billion Degree of Freedoms (DOFs). By leveraging a high-order unstructured solver and its portability to large heterogeneous parallel systems, we have progressed towards solving the grand challenge problem outlined by NASA [1], which involves a time-dependent simulation of a complete engine, incorporating all the aerodynamic and heat transfer components.

The Lattice Boltzmann Method (LBM) is a relatively new class of Computational Fluid Dynamics methods. In this paper, we report our work on SunwayLB, which enables LBM based solutions aiming for industrial applications. We propose several techniques to boost the simulation speed and improve the scalability of SunwayLB, including a customized multi-level domain decomposition and data sharing scheme, a carefully orchestrated strategy to fuse kernels with different performance constraints for a more balanced workload, and optimization strategies for assembly code, which bring up to 137x speedup. Based on these optimization schemes, we manage to perform the largest direct numerical simulation which involves up to 5.6 trillion lattice cells, achieving 11,245 billion cell updates per second (GLUPS), 77% memory bandwidth utilization and a sustained performance of 4.7 PFlops. We also demonstrate a series of computational experiments for extreme-large scale fluid flow, as examples of real-world applications, to check the validity and performance of our work. The results show that SunwayLB is competent for a practical solution for industrial applications.