SEARCH · Search NASA
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
An approach is disclosed that tracks memory transactions by a node. The approach establishes a transaction processing state corresponding to common virtual addresses accessed by a processing threads. Transactions are executed by the threads. A selected transaction is allowed to complete. In response to detecting a conflict in the transaction processing state, completion of a non-selected transaction is inhibited.
A tamper-resistant threaded fastener including a circular head having a slot formed in the head transverse to the longitudinal axis, the slot having an enlarged region intermediate an apex and a base of the slot. A cap member includes a head having a drive feature formed therein, the head extending to a first shank portion, extending to an enlarged second shank portion, extending to a foot. The cap member is insertable in the slot only from a direction transverse to the longitudinal axis. During rotational installation of the fastener and cap member in a threaded counterbored opening for securing a barrier for protecting an asset, the foot shears inside the slot at a predetermined rotational drive force, the cap member being rotatable in the opening and protectively covering the fastener head.
A processing system scales power to memory and memory channels based on identifying causes of stalls of threads of a wavefront. If the cause is other than an outstanding memory request, the processing system throttles power to the memory to save power. If the stall is due to memory stalls for a subset of the memory channels servicing memory access requests for threads of a wavefront, the processing system adjusts power of the memory channels servicing memory access request for the wavefront based on the subset. By boosting power to the subset of channels, the processing system enables the wavefront to complete processing more quickly, resulting in increased processing speed. Conversely, by throttling power to the remainder of channels, the processing system saves power without affecting processing speed.
With an increase of control over crystalline defects, metallorganic vapor phase epitaxy (MOVPE)-grown III-V-on-Si multijunction solar cells have seen rapid increases in efficiency in recent years, pointing to a promising path to lower cost III-V solar cells. However, the cost of chemo-mechanical polishing the Si wafers to prepare them for epitaxy is high. The use of V-groove nanopatterns enables similar defect reduction to that achieved on planar wafers, but the nanopatterns can be fabricated with a low-cost process. While V-grooves offer advantages over planar Si, they add complexity to the growth process. In particular, coalescence can cause the formation of threading dislocations, and the highly-directional growth conditions required for coalescence are unusual for MOVPE. We have studied the coalescence of GaP films nucleated directly on V-groove Si by MOVPE. We observed that for optimized growth conditions (V/III=5,000 and T=800 C) two growth modes were possible, and the resulting morphology depended on the exact geometry of the SiNx cap used to cover the (0 0 1)-oriented Si at the tops of the grooves. For caps with a width >100 nm, noncoalescing, nano ridge-like growth terminating in f1 1 1g facets was observed. For narrower caps, coalescence with an RMS roughness of 0.2 nm as measured by atomic force microscopy was observed. We will discuss mechanisms responsible for this phenomenon, including the role of Si from the substrate surface. The dislocation dynamics of this system were studied with electron channeling contrast imaging (ECCI) and transmission electron microscopy (TEM). We find that V-grooves do not block dislocation glide; ECCI measurements show misfit dislocations greater than 10 micrometers long observed to continue perpendicularly across neighboring V-grooves. In addition, all threading dislocations appear to lie on f1 1 1g planes, consistent with the GaP relaxing via glided-in glissile dislocations. The dislocation dynamics and morphological evolution of the coalescence of GaP on Si, possible mechanisms behind the observed phenomenons, and further dislocation mitigation strategies for these materials will be presented.
The present invention provides a method of explosively forming a helical tube from at least one thin-walled cylinder using a tooling assembly. The method includes inserting the at least one thin-walled cylinder into a die of the tooling assembly. The die surrounds the at least one thin-walled cylinder and includes an interior surface that defines a helical thread pattern. The method further includes surrounding the at least one thin-walled cylinder and the die with a casing of the tooling assembly. A cavity is defined by the casing and the thin-walled cylinder. The method further includes positioning an explosive charge within the cavity. The method additionally includes at least partially submerging the tooling assembly. The method further includes detonating the explosive charge. As a result, the at least one thin-walled cylinder is formed into a helical tube that corresponds with helical thread pattern of the interior surface of the die.
Abstract Dynamic (temporal) graphs are a convenient mathematical abstraction for many practical complex systems including social contacts, business transactions, and computer communications. Community discovery is an extensively used graph analysis kernel with rich literature for static graphs. However, community discovery in a dynamic setting is challenging for two specific reasons. Firstly, the notion of temporal community lacks a widely accepted formalization, and only limited work exists on understanding how communities emerge over time. Secondly, the added temporal dimension along with the sheer size of modern graph data necessitates new scalable algorithms. In this paper, we investigate how communities evolve over time based on several graph metrics under a temporal formalization. We compare six different algorithmic approaches for dynamic community detection for their quality and runtime. We identify that a vertex-centric (local) optimization method works as efficiently as the classical modularity-based methods. To its advantage, such local computation allows for the efficient design of parallel algorithms without incurring a significant parallel overhead. Based on this insight, we design a shared-memory parallel algorithm DyComPar , which demonstrates between 4 and 18 fold speed-up on a multi-core machine with 20 threads, for several real-world and synthetic graphs from different domains.
A digital twin must seek to represent all applicable functional components of the system of interest. Different expertise is required for understanding the physical system being modeled than the skills needed for transforming those models into a functional digital twin through physics modeling, machine learning analysis, and visualization. The diversity of knowledge requires a multi-disciplinary team to ensure all system details are captured. Team members also need a method to verify that the data they generate within their domain can be effectively communicated to professionals in other fields. To address this challenge, this work provides an approach for developing a digital twin framework to remotely monitoring nuclear facilities. Through this, general knowledge of the framework is presented along with two examples to solidify the process. The AGN-201 digital twin and microreactor digital twins provide varying levels of complexity in a potential nuclear facility, where common threads are identified and lessons learned are provided. The goal of this research is to aid future researchers by providing a formula for a successful digital twin and in turn reducing the development time of nuclear system digital twins, specifically for remote monitoring.
Here, we demonstrate Ga 1-x In x P compositionally graded buffers (CGBs) grown on GaAs with lattice constants between GaAs and InP by hydride vapor phase epitaxy (HVPE). Growth rates were up to ~1 μm/min and threading dislocation density (TDD) was as low as 1.0 x 10 6 cm -2 . We studied the effect of substrate offcut direction, growth rate, and strain grading rate on CGB defect structure. We compared the effect of a “dynamic grading” style, which creates compositional interfaces via mechanical transfer of a substrate between two growth chambers, vs. “static grading” where the CGB grows in a single chamber. Dynamic grading yielded smoother grades with higher relaxation, but TDD was not significantly different between the two styles. Substrate offcut direction was the most important factor for obtaining CGBs with low defect density. (001) substrates offcut towards (111)B yielded smoother CGBs with lower TDD compared to CGBs grown on substrates offcut towards (111)A. Transmission electron microscopy of static and dynamic CGBs grown on A and B-offcuts only found evidence of phase separation in a static A-offcut CGB, indicating that the B offcut limits phase separation, which in turn keeps TDD low. Reduced growth rate led to the appearance CuPt-type atomic ordering, which affected the distribution of dislocations on the active glide planes but did not alter TDD. Higher growth rates led to smoother CGBs and did not appreciably increase TDD as otherwise predicted by steady-state models of plastic relaxation. These results show HVPE’s promise for lattice-mismatched applications and low-c ost InP virtual substrates on GaAs
We investigate strategies to suppress phase separation and reduce threading dislocation density (TDD) in AlGaInAs compositionally graded buffers (CGBs) that span the lattice constant range from GaAs to InP. Combining the results from high resolution x-ray diffraction, cathodoluminescence, transmission electron microscopy, and photovoltaic device measurements, we correlate the choices of epitaxial growth conditions with the defect structure of the CGBs and subsequent device performance. Both the use of substrates with high misorientation off (100) toward the (111)A plane and Zn doping instead of Si doping are shown to suppress phase separation and reduce TDD. We demonstrate a 0.74 eV GaInAs device grown on a GaAs substrate offcut 19.5° toward (111)A using a Zn-doped AlGaInAs CGB with TDD = 3.5 ± 0.2 × 10 6 cm -2 that has a bandgap-open circuit voltage offset of only 0.434 V measured under the AM1.5G solar spectrum. We characterized this device under high-intensity irradiance from a 1570 nm laser and measured a 31.9% peak laser power conversion efficiency at 3.6 W/cm 2 irradiance. These results provide a roadmap to the manufacture of laser- and thermal-power conversion devices with the performance and cost-effectiveness needed to drive adoption of these technologies at scale.
Nuclear energy systems present unique challenges in terms of ensuring safety, reliability, and efficiency during their design and operation. Early fault detection is critical for mitigating risks and fostering system resilience. However, current methods often fall short at identifying faults during early stages, potentially leading to costly delays and safety risks. The present work proposes a comprehensive digital engineering approach that leverages digital twins, digital threads, model-based systems engineering, artificial intelligence, and immersive extended reality to support early fault detection in nuclear systems. Through a series of case studies, we highlight specific gaps in the fault detection mechanisms of traditional nuclear design and operation processes, then demonstrate a suite of solutions we are working to implement to address these shortcomings in similar projects. Our findings suggest that a digital engineering approach to design and operation can significantly improve fault detection, ultimately leading to reductions in risk.
The National Reactor Innovation Center (NRIC) is leading a transformative initiative to accelerate advanced reactor deployment by fundamentally reimagining how nuclear safety basis documentation is developed, reviewed, and maintained. Traditional Documented Safety Analysis (DSA) processes for DOE-authorized facilities rely on static, document-centric workflows that consume significant time and resources, exemplified by recent major licensing efforts requiring hundreds of thousands of staff hours and millions of pages of documentation review. These conventional approaches create barriers to the rapid, cost-effective deployment of advanced reactors that America's future energy needs demand. NRIC's DOE Authorization Digital Transformation Project addresses these challenges through an innovative framework that integrates artificial intelligence (AI), digital engineering, and systems-based data management into a cohesive digital ecosystem. This white paper presents NRIC's methodology for evaluating AI-enabled document generation capabilities within this broader digital infrastructure, using the Demonstration of Microreactor Experiments (DOME) facility as a pilot case study. The evaluation will assess an AI tool's ability to generate a Preliminary Documented Safety Analysis (PDSA) through progressive integration stages—from standalone document processing to full digital thread connectivity—while maintaining rigorous verification, validation, and regulatory acceptance standards. By establishing dynamic, traceable connections between design data and safety documentation, NRIC's approach has the potential to reduce both document development time and regulatory review cycles by as much as 50%, while simultaneously improving accuracy, consistency, and traceability. This initiative represents a critical step toward establishing reusable digital infrastructure that reactor developers can leverage to accelerate their path from concept to commercial operation, directly supporting NRIC's mission to demonstrate and deploy advanced nuclear energy technologies.
Large-scale enterprises execute a full range of missions to satisfy objectives. Healthy enterprises should acquire and maintain mission awareness, which requires reasoning over mission models that identify the structure and character of missions along with their related cyber resources. Models are typically constructed by subject matter experts using a time- and labour-intensive manual process. This paper describes a novel framework comprising high-level processes that model mission essential functions and mission threads as well as recognize active instances. This framework enables data-driven, semi-automated modelling to construct accurate, timely mission representations necessary for mission assurance and mission mapping.
Systems engineering is of utmost importance for the success of high-cost, high-complexity megaprojects, which are common in the energy sector. However, the traditional document-centric systems engineering approach tends to be labor-intensive and time-consuming, which has inhibited its full adoption despite proven metrics on its return on investment. However, with the modern approach of digital engineering and technological advancements in artificial intelligence (AI) technologies, the barriers to systems engineering adoption can finally be broken. This paper goes through the systems engineering V-model for lifecycle management and assesses the current state of implementation of digital engineering (especially, mod-el-based systems engineering, digital twins, and digital threads) and AI for each step. It was observed that a combination of digital engineering and AI is being used across different industries to accelerate and optimize systems engineering processes such as concept development, requirements management, architecture definition, system development, verification and validation, operations, and maintenance. Specifically in the energy sector, AI-augmented digital engineering has shown initial potential in accelerated development and deployment, performance optimization, anomaly detection, predictive maintenance, and configuration management. However, challenges remain in integrating DE and AI into an end-to-end system lifecycle management ecosystem safely and reliably. Addressing these challenges and continuously developing impactful tools will enable fast, efficient, and high-frequency deployment of power generation capabilities to keep up with the world’s energy demands and build energy security.
Systems engineering is of utmost importance for the success of high-cost, high-complexity megaprojects, which are common in the energy sector. However, the traditional document-centric systems engineering approach tends to be labor-intensive and time-consuming, which has inhibited its full adoption despite proven metrics on its return on investment. However, with the modern approach of digital engineering and technological advancements in artificial intelligence (AI) technologies, the barriers to systems engineering adoption can finally be broken. This paper goes through the systems engineering V-model for lifecycle management and assesses the current state of implementation of digital engineering (especially, mod-el-based systems engineering, digital twins, and digital threads) and AI for each step. It was observed that a combination of digital engineering and AI is being used across different industries to accelerate and optimize systems engineering processes such as concept development, requirements management, architecture definition, system development, verification and validation, operations, and maintenance. Specifically in the energy sector, AI-augmented digital engineering has shown initial potential in accelerated development and deployment, performance optimization, anomaly detection, predictive maintenance, and configuration management. However, challenges remain in integrating DE and AI into an end-to-end system lifecycle management ecosystem safely and reliably. Addressing these challenges and continuously developing impactful tools will enable fast, efficient, and high-frequency deployment of power generation capabilities to keep up with the world?s energy demands and build energy security.
Abstract The prospect of 2‐dimensional electron gases (2DEGs) possessing high mobility at room temperature in wide‐bandgap perovskite stannates is enticing for oxide electronics, particularly to realize transparent and high‐electron mobility transistors. Nonetheless only a small number of studies to date report 2DEGs in BaSnO 3 ‐based heterostructures. Here, 2DEG formation at the LaScO 3 /BaSnO 3 (LSO/BSO) interface with a room‐temperature mobility of 60 cm 2 V −1 s −1 at a carrier concentration of 1.7 × 10 13 cm –2 is reported. This is an order of magnitude higher mobility at room temperature than achieved in SrTiO 3 ‐based 2DEGs. This is achieved by combining a thick BSO buffer layer with an ex situ high‐temperature treatment, which not only reduces the dislocation density but also produces a SnO 2 ‐terminated atomically flat surface, followed by the growth of an overlying BSO/LSO interface. Using weak beam dark‐field transmission electron microscopy imaging and in‐line electron holography technique, a reduction of the threading dislocation density is revealed, and direct evidence for the spatial confinement of a 2DEG at the BSO/LSO interface is provided. This work opens a new pathway to explore the exciting physics of stannate‐based 2DEGs at application‐relevant temperatures for oxide nanoelectronics.
Abstract Inverted metamorphic Ga 0.3 In 0.7 As photovoltaic converters with sub‐0.60 eV bandgaps grown on InP and GaAs are presented. Threading dislocation densities are 1.3 ± 0.6 × 10 6 and 8.9 ± 1.7 × 10 6 cm −2 on InP and GaAs, respectively. The devices generate open‐circuit voltages of 0.386 and 0.383 V, respectively, under irradiance producing a short‐circuit current density of ≈10 A cm −2 , yielding bandgap‐voltage offsets of 0.20 and 0.21 V. Power and broadband reflectance measurements are used to estimate thermophotovoltaic (TPV) efficiency. The InP‐based cell is estimated to yield 1.09 W cm −2 at 1100 °C versus 0.92 W cm −2 for the GaAs‐based cell, with efficiencies of 16.8 versus 9.2%. The efficiencies of both devices are limited by sub‐bandgap absorption, with power weighted sub‐bandgap reflectances of 81% and 58%, respectively, the majority of which is assumed to occur in the graded buffers. The 1100 °C TPV efficiencies are estimated to increase to 24.0% and 20.7% in structures with the graded buffer removed, if previously demonstrated reflectance is achieved. These devices also have application to laser power conversion in the 2.0–2.3 µm atmospheric window. Peak laser power converter efficiencies of 36.8% and 32.5% are estimated under 2.0 µm irradiances of 1.86 and 2.81 W cm −2 , respectively.
The development of integrated systems that mimic the multi-stage stiffness change of marine animals such as the sea cucumber requires the design of molecularly tailored structures. Herein, we used an integrated biomimicry design to fabricate a sea cucumber mimic using sidechain polypseudorotaxanes with tunable nano-to-macroscale properties. A series of polyethylene glycol (PEG)-based sidechain copolymers were synthesized to form sidechain polypseudorotaxanes with α-cyclodextrins (α-CDs). By tailoring the copolymers’ molecular weights and their PEG grafting densities, we rationally tuned the sizes of the formed polypseudorotaxanes crystalline domain and the physical crosslinking density of the hydrogels, which facilitated 3D printing and the mechanical adaptability to these hydrogels. After 3D printing and photo-crosslinking, the obtained hydrogels exhibited large tensile strain and broad elastic-to-plastic variations upon α-CD (de)threading. Finally, these discoveries enabled a successful fabrication of a sea cucumber mimic, demonstrating multi-stage stiffness changes.
The Roofline performance model provides an intuitive approach to identify performance bottlenecks and guide performance optimization. However, the classic FLOP-centric approach is inappropriate for the emerging applications that perform more integer operations than floating point operations. In this article, we reintroduce our Instruction Roofline Model on NVIDIA GPUs and expand our evaluation of it. The Instruction Roofline incorporates instructions and memory transactions across all memory hierarchies together, and provides more performance insights than the FLOP-oriented Roofline Model, that is, instruction throughput, stride memory access patterns, bank conflicts, and thread predication. We use our Instruction Roofline methodology to analyze eight proxy applications: HPGMG from AMReX, Matrix Transpose benchmarks, ADEPT from MetaHipMer's sequence alignment phase, EXTENSION from MetaHipMer's local assembly phase, CUSP, cuSPARSE, cudaTensorCoreGemm, and cuBLAS. We demonstrate the ability of our methodology to understand various aspects of performance and performance bottlenecks on NVIDIA GPUs and motivate code optimizations.