Search NASA⌕ Search

Engineering topics

Xiao, Yao

Publications and source records attributed to Xiao, Yao.

6 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Overcoming the Damping–Elasticity Paradox via 3D‐Printed NiTiSn Nanocomposite

Abstract Developing high damping alloys (HDAs) with large elastic strain has attracted growing attention due to the increasing demand for energy absorption with overload reliability and reusability. However, damping capacity inherently conflicts with elasticity, because the former requires a liable movement of crystal defects while the latter opposite. To deal with the damping‐elasticity paradox, the advantage of pseudobinary eutectic reaction and rapid cooling of laser powder bed fusion is taken to fabricate a bulk NiTiSn nanocomposite with a two‐level hierarchical structure. The first‐level architecture is composed of martensitic NiTi nanolamellae and reinforced Ti 3 Sn nanolamellae. In addition to lattice strain matching and lamellar boundary strengthening, a novel mechanism of martensite reorientation mediated by reversible stress‐induced detwinning‐twinning is activated to generate large elastic strain. A high density of nanotwins and nanodomains within NiTi nanolamellae constitute the second‐level architecture, which provides pronounced internal friction for high damping capacity. As a result, our NiTiSn nanocomposite exhibits a record‐high integration of damping capacity (tanδ > 0.10) and elastic strain (exceeding 4.5%), as well as superb stability under cyclic overload. This research not only represents a major breakthrough in achieving HDAs with outstanding damping and elastic strain but also offers a novel paradigm for high‐performance functional and structural materials.

Chemistry↗
DOE OSTI2024

Atomically precise engineering of spin–orbit polarons in a kagome magnetic Weyl semimetal

Atomically precise defect engineering is essential to manipulate the properties of emerging topological quantum materials for practical quantum applications. However, this remains challenging due to the obstacles in modifying the typically complex crystal lattice with atomic precision. Here, we report the atomically precise engineering of the vacancy-localized spin–orbit polarons in a kagome magnetic Weyl semimetal Co 3 Sn 2 S 2 , using scanning tunneling microscope. We achieve the step-by-step repair of the selected vacancies, leading to the formation of artificial sulfur vacancies with elaborate geometry. We find that that the bound states localized around these vacancies undergo a symmetry dependent energy shift towards Fermi level with increasing vacancy size. As the vacancy size increases, the localized magnetic moments of spin–orbit polarons become tunable and eventually become itinerantly negative due to spin–orbit coupling in the kagome flat band. These findings provide a platform for engineering atomic quantum states in topological quantum materials at the atomic scale.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

GAHLS: an optimized graph analytics based high level synthesis framework

The urgent need for low latency, high-compute and low power on-board intelligence in autonomous systems, cyber-physical systems, robotics, edge computing, evolvable computing, and complex data science calls for determining the optimal amount and type of specialized hardware together with reconfigurability capabilities. With these goals in mind, we propose a novel comprehensive graph analytics based high level synthesis (GAHLS) framework that efficiently analyzes complex high level programs through a combined compiler-based approach and graph theoretic optimization and synthesizes them into message passing domain-specific accelerators. This GAHLS framework first constructs a compiler-assisted dependency graph (CaDG) from low level virtual machine (LLVM) intermediate representation (IR) of high level programs and converts it into a hardware friendly description representation. Next, the GAHLS framework performs a memory design space exploration while account for the identified computational properties from the CaDG and optimizing the system performance for higher bandwidth. The GAHLS framework also performs a robust optimization to identify the CaDG subgraphs with similar computational structures and aggregate them into intelligent processing clusters in order to optimize the usage of underlying hardware resources. Finally, the GAHLS framework synthesizes this compressed specialized CaDG into processing elements while optimizing the system performance and area metrics. Evaluations of the GAHLS framework on several real-life applications (e.g., deep learning, brain machine interfaces) demonstrate that it provides 14.27× performance improvements compared to state-of-the-art approaches such as LegUp 6.2.

97 MATHEMATICS AND COMPUTING↗