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As an open-sourced instruction set and being flexible in hardware extension, RISC-V begins its pace to enter the world of high performance computing. One of the distinguished feature of processing units adopting RISC-V is its ability to add custom circuits with special purpose accelerators. As the artificial general intelligence becomes practical, AI accelerators become an indispensable part of computing devices, where RISC-V is a great fit for the CPU to glue accelerators together. A system of chip designed by Alibaba T-head is one of the early chip in the massive production adopting RISC-V CPU, where the CPU, named Xuantie-910, has a high performance design with 128-bit RISC-V vector processing units, which are designed for accelerating AI applications. OpenMC has been adapted to run on Xuantie-910. In the Monte Carlo method for reactor physics, fetching the neutron cross sections is the hotspot that takes the majority of the computational burden. The traditional point-wise cross sections are slow because of memory latency caused by accessing many nonconsecutive memory addresses. An AI model for cross section is hence proposed. With 2.2 KB of runtime size, the smallest in the published work, the data can be fetched entirely in the L1 cache during on-the-fly cross section evaluation through single memory read. The in-house AI model also covers the entire energy range, unlike only the resonance range is supported in previous work. So, the effects from memory latency is minimized. The average relative error in AI modeled U-238 elastic cross section is 0.6% from point-wise cross section. With a modified version of OpenMC on Apple M3 Max, for a VERA pin-cell problem, compared to the point-wise cross section, the adoption of AI modeled cross section reduces the total runtime by 7%, although the runtime for calculating U-238 elastic cross section causes 40% more runtime. The adoption of AI modeled U-238 elastic cross section leads to K-effective 302 pcm higher than the case of adoption of point-wise cross sections. Advantage of AI model has been verified. With AI modeled cross section, the neutron slowing down problems with pure elastic scattering on U-238 has been studied on Xuantie-910. The average relative error in 65,536 group fluxes is about 0.9% from using point-wise cross section. However, with accelerating with the 128-bit vector processing units, the performance degrades by 35%, because of the narrow 64-bit load and store interface to the vector register files. The performance with Al modeled cross section is about 1/4 of the case with point-wise cross sections. In addition, the 1,024-bit width Ara RISC-V vector processing has been used to study the cost of AI modeled cross section evaluation. Being able to access the open-sourced hardware design in SystemVerilog, cycle accurate circuit simulation is performed. Using the vector processing units, the cost is reduced to 65% of the case using scalar instructions. The 128-bit load and store interface to vector processing units is a major contributor to the speeding up. The width of the load and store interface to vector processing units should be the main optimization factor in chip design to accelerate the AI modeled cross section evaluation.

In simulation of nuclear reactor physics using the Monte Carlo neutron transport method on GPUs, the sorting of particles plays a significant role in performance of calculation. Traditionally, CPUs and GPUs are separated devices connected at low data transfer rate and high data transfer latency. Emerging computing chips tend to integrate CPUs and GPUs. One example is the Apple silicon chips with unified memory. Such unified memory chips have opened doors for new strategies of collaboration between CPUs and GPUs for Monte Carlo neutron transport. Sorting particles on CPU and transport on GPU is an example of such new strategy, which has been suffering the high CPU-GPU data transfer latency on the traditional devices with separated CPU and GPU. The finding is that for the Apple M2 max and M3 max chip, sorting on CPU leads to better performance per power than sorting on GPU for the ExaSMR whole core benchmark problems and the HTR-10 high temperature gas reactor fuel pebble problem. The partially sorted particle order has been identified to contribute to the higher performance with CPU sort than GPU. The in-house code using both CPU and GPU achieves 7.6 times (M3 max) power efficiency that of OpenMC on CPU for ExaSMR whole core benchmark with depleted fuel, and 130 times (M3 max) for HTR-10 fuel pebble benchmark with depleted fuel.

Recent development of quantum computing has been extended to nuclear physics. Researchers have demonstrated that the quantum simulation of deuteron ground state energy using the variational quantum eigenvalue solver. In this existing work, only 2 and 3 qubits are used. In this work, the theory of higher order expansion of the deuteron ground state Hamiltonian has been established. On a classical quantum simulator, the convergence of the ground state energy as the number of qubits increases has been verified up to 22 qubits.

An integrated constructed wetland-microbial electrolysis cell (ICW-MEC) system was investigated for nitrogen removal under different pollution loads, hydraulic loads (HLRs), and aeration conditions. The treatment performance of each unit and the microbial community characteristics for nitrogen removal were elucidated. The results showed that, on average, 80% of NH4+-N, around 70% of nitrate nitrogen (NO3−-N), and 70% of total nitrogen (TN) were removed by the system under three pollution loads, with less influence by pollution loads. The high removal efficiencies of NH4+-N (81.8%), NO3−-N (71.4%), and TN (72.8%) indicated tolerable to high HLRs. The intermittent aeration negatively affected NH4+-N removal, while increasing NO3−-N and TN removals by 3.2–13.0% and 3.7–16.7%. The contribution efficiencies of the secondary unit to the removal of NH4+-N, NO3−-N, TN, and total organic carbon (TOC) reached 47.4%, 55.0%, 45.9%, and 38.8%, respectively. The distinct microbial communities existed in various units of the ICW-MEC system, which were strongly affected by environmental factors and shaped by diverse fillers and structures of the system. The dominant bacteria contributed to the efficient nitrogen removal performance of the ICW-MEC system. The three units exerted their advantages to ensure efficient and stable system operation. [ABSTRACT FROM AUTHOR]

Perfluorosulfonated anionic ionomers are known for their robust ion conductivity and chemical and mechanical stability. However, the structure and transport property degradation of perfluorinated cationic polymers (PfCPs) are not well understood. Herein, we propose an ion-exchange strategy to identify the structural degradation, ion transport mechanisms, and architectural reformation of PfCPs. Particularly, we demonstrate that the utility of a –SO2–N+ strategy employing the Menshutkin reaction cannot yield reliable PfCPs and anion-exchange membranes, but can yield an unreliable zwitterionic intermediate (cations–anions molar ratio is approximately 7.6%). Moreover, the degradation products were efficiently reformed as proton exchange membranes (PEMs), and the as-reformed PEMs achieved an ion-exchange capacity (IEC) value (0.89 mmol g−1), meanwhile retaining more than 94.7% of their initial capacity. Furthermore, the fuel cell assembled with reformed PEMs displayed a power density of 0.91 Wcm−2 at 2.32 A cm−2, which was 90.1% of that of the robust perfluorosulfonic acid PEMs. Our combined findings shed some fresh light on the state of understanding of the structure–property relationship in PfCPs. [ABSTRACT FROM AUTHOR]

Humanoid robots have attracted much attention by virtue of their compatibility with human environments. However, biped humanoids with immense promise still cannot function steadily and reliably in real‐world settings in the current state. Hence, rationally combining a humanoid robot with different stable mobile platforms is a favoured solution for diverse scenarios. Here, a new versatile humanoid robot platform, aiming to provide a generic solution that can be flexibly deployed in diverse scenarios, for example, indoors and fields is presented. Versatile humanoid robot platform incorporates multimodal perception, and extensible interfaces on hardware and software, allowing it to be rapidly integrated with different mobile platforms and end‐effectors, only through easy‐to‐assemble interfaces. Additionally, the platform has achieved impressive integration, lightness, dexterity, and strength in its class, with human‐like size and rich perception, targeted to have human‐intelligent manipulation skills for human‐engineered environments. Overall, this article elaborates on the reasoning behind the design choices, and outlines each subsystem. Lastly, the essential performance of the platform is successfully demonstrated in a set of experiments with precise and dexterous manipulation, and human–robot collaboration requirements. [ABSTRACT FROM AUTHOR]

In this study, we investigate the use of natural additives (biopolymers) resistant to scouring. To this end, three natural substances, Kathira, sodium alginate, and guar gum, have been utilized as additives resistant to scouring, and we examine their mechanical performance, resistance to scouring, and the properties of fresh concrete including slump test, setting time, and ultimately shrinkage test. For this purpose, a total of 12 cylindrical specimens with dimensions of 15 by 30 cm were prepared for 28-day compressive strength test, and 12 cylindrical specimens with dimensions of 15 by 30 cm were prepared for 28-day indirect tensile strength test. Additionally, 12 concrete beams with dimensions of 10 by 10 by 35 cm were fabricated for a 28-day flexural strength test. All laboratory specimens were submerged in lime-saturated water for hydration for a period of 28 days for maintenance and preservation. The results indicate that all three biopolymers improve resistance to scouring, and, two substances enhance compressive, tensile, and flexural strength. Furthermore, all of them lead to a reduction in concrete shrinkage. [ABSTRACT FROM AUTHOR]

This study investigated the anchoring effect of wedge anchorages on prestressed basalt fiber–reinforced polymer (BFRP) laminates. Compared with traditional carbon fiber–reinforced polymer (CFRP) laminates, BFRP laminates have a larger ultimate fracture strain, lower elastic modulus, and smaller prestress loss, making them cost-effective prestressed materials. Fifteen fiber-reinforced polymer (FRP) laminate–anchorage assemblies were prepared to investigate the effects of FRP type, anchorage type, mat type, and presetting load level on the static tensile behavior of FRP laminate–anchorage assemblies. The differences in the failure modes, load versus displacement (L–D) curves, and strain development of the FRP laminates were analyzed. The results demonstrated that, first, the anchorage efficiency of the CFRP laminate–anchorage assembly was higher than that of BFRP. Since the elastic modulus and transverse shear strength of the BFRP laminates were much lower than those of the CFRP laminates, the application of the traditional CFRP anchorage easily damaged the BFRP laminates. Therefore, a specific design and optimization are required for BFRP laminate anchorages. Second, an anchorage with a retracted stressing end wedge was prone to obtain higher normal stress and thus achieve a higher anchoring capacity. Third, the sandpaper mat improved the friction between the FRP laminate and wedges and alleviated the stress concentration phenomenon of the FRP laminate at the stressing end of the anchorage. Finally, an increased presetting load on the wedges could slightly improve the assemblies' ultimate bearing capacity and stiffness; however, the enhanced presetting load aggravated the stress concentration phenomenon at the stressing end of the anchorage. [ABSTRACT FROM AUTHOR]