SUMMIT: Standard Unified Modeling Mapping and Integration Toolkit: Overview and Wildfire Applications.
Abstract not provided.
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.
Abstract not provided.
Explore the source record for details and available documents.
To meet the extreme compute demands for deep learning across commercial and scientific applications, dataflow accelerators are becoming increasingly popular. While these“domain-specific” accelerators are not fully programmable like CPUs and GPUs, they retain varying levels of flexibility with respect to data orchestration, i.e., dataflow and tiling optimizations to enhance efficiency. There are several challenges when designing new algorithms and mapping approaches to execute the algorithms for a target problem on new hardware. Previous works have addressed these challenges individually. To address this challenge as a whole, in this work, we present an HW-SW co-design ecosystem for spatial accelerators called Union within the popular MLIR compiler infrastructure. Our framework allows exploring different algorithms and their mappings on several accelerator cost models. Union also includes a plug-and-play library of accelerator cost models and mappers which can easily be extended. The algorithms and accelerator cost models are connected via a novel mapping abstraction that captures the map space of spatial accelerators which can be systematically pruned based on constraints from the hardware, workload, and mapper. We demonstrate the value of Union for the community with several case studies which examine offloading different tensor operations (CONV/GEMM/Tensor Contraction) on diverse accelerator architectures using different mapping schemes.
LID degradation involves not only creation of ~ 10^12 cm^-3 recombination centers, but also ~ 10^16 cm^-3 shallow negative-U traps. In Ga-doped Si, LID EPR defects don't appear, but some traps are still created. LeTID: Si DB and H-hyperfine EPR signatures. We postulate that the defect responsible for LeTID is a partially hydrogenated (multivacancy) with a Si dangling bond and H in the vicinity. O involvement is possible yet unclear. We prove that H is related to the structure of the LeTID defect with isotope experiments and its EPR signal is comparable and linear with the Si DB signal upon LeTID degradation. Working on simulating these results with DFT to obtain more detailed defect structure.
Abstract not provided.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Electric vehicle (EV) adoption will expand beyond light-duty vehicles charging at lower-power public locations and family residences to include medium- and heavy-duty applications with more powerful charging at public stations and private depots. The Vehicle Grid Integration (VGI) and Smart Charge Management (SCM) pillar in EVs@Scale supports multiple projects. The FUSE project is developing and evaluating enabling technologies, charging solutions, and infrastructure design approaches to investigate the intricacies and potential benefits of vehicle grid integration and the application of smart charge management to charging of electric vehicles at scale. This presentation summarizes the findings of this research in 2024.
Explore the source record for details and available documents.
In superconducting quantum circuits, decoherence improvements are frequently obtained through process interventions that simultaneously modify surface chemistry, microstructural topology, and device geometry, leaving mechanistic attribution structurally underdetermined. Predictive materials engineering requires measurable structural statistics to be separated from geometry-dependent coupling coefficients into independently testable factors. We introduce the concept of classical and quantum microstructure. In that context, we formulate a channel-wise separable framework for decoherence in superconducting transmon qubits in which each loss channel is described by a reduced prescriptor. Here, a channel-specific microstructural state variable is determined independently of device geometry, and a geometry-dependent coupling functional is computable from field solutions without reference to surface chemistry. We derive this product form from a spatially resolved kernel representation and establish a perturbative separability criterion that defines the regime where independent variation of the variables is valid. The framework specifies five prescriptor classes for dominant loss pathways in transmon-class devices. Falsifiability is operationalized through a pre-committed 2x2 experimental protocol in which the variables must satisfy independent ratio checks within propagated uncertainty. A Minimum-Dataset Specification standardizes reporting for cross-laboratory inference. Part I establishes the conceptual and mathematical architecture; coordinated experimental validation is reserved for Part II.
This paper presents a deep generative model-based design methodology for tailoring the structural stochasticity of microstructures. Although numerous methods have been established for designing deterministic (periodic) or stochastic microstructures, a systematic design approach that allows the unified treatment of both deterministic and stochastic microstructure design domains has yet to be created. The proposed methodology resolves this issue by learning a unified feature space that embodies diverse structural patterns with continuously varying stochasticity levels. A highly diverse microstructure database is established to incorporate various types of deterministic and stochastic microstructure patterns. A property-aware deep generative model is proposed to learn a unified feature space of the structural characteristics, as well as the relationship between structure features and properties of interest. Autoencoder (AE), Variational Autoencoder (VAE), and Adversarial Autoencoder (AAE) are compared to understand their relative merits in the property-aware learning of the unified feature space. Microstructural designs with tailorable stochasticity and properties are obtained by searching the unified feature space. Multiple design cases are presented to demonstrate the capability of designing microstructures for structural stochasticity and properties. Furthermore, the proposed method is employed to create stochastically graded structures, which manipulate the mechanical behaviors by varying the local stochasticity of the structure.
This is Part 2 of a trio of works intended to provide a unifying framework in which conservative and optimistic synchronization for parallel discrete event simulations can be freely and transparently combined in the same logical process on an event-by-event basis. Here, in this article, we continue the outline of an approach called Unified Virtual Time (UVT) that was introduced in Part 1, showing in detail via two extended examples how conservative synchronization can be refactored and combined with optimistic synchronization in the UVT framework. We describe UVT versions of both a basic time windowing algorithm called Unified Simple Time Windows and a refactored version of the Chandy-Misra-Bryant Null Message algorithm called Unified CMB.
Abstract Soil organic nitrogen (N) is a critical resource for plants and microbes, but the processes that govern its cycle are not well-described. To promote a holistic understanding of soil N dynamics, we need an integrated model that links soil organic matter (SOM) cycling to bioavailable N in both unmanaged and managed landscapes, including agroecosystems. We present a framework that unifies recent conceptual advances in our understanding of three critical steps in bioavailable N cycling: organic N (ON) depolymerization and solubilization; bioavailable N sorption and desorption on mineral surfaces; and microbial ON turnover including assimilation, mineralization, and the recycling of microbial products. Consideration of the balance between these processes provides insight into the sources, sinks, and flux rates of bioavailable N. By accounting for interactions among the biological, physical, and chemical controls over ON and its availability to plants and microbes, our conceptual model unifies complex mechanisms of ON transformation in a concrete conceptual framework that is amenable to experimental testing and translates into ideas for new management practices. This framework will allow researchers and practitioners to use common measurements of particulate organic matter (POM) and mineral-associated organic matter (MAOM) to design strategic organic N-cycle interventions that optimize ecosystem productivity and minimize environmental N loss.
This paper introduces CAN to ROS, a model-based code generation tool used in development, testing, and deployment of a heterogeneous fleet of vehicles with robotic sensing in ROS. Code generation supports two main features: (1) self-configuration for deployment in a heterogeneous vehicle fleet, and (2) quick iteration for testing and development of reading vehicle sensors and robotic control. This tool features the ability to detect the vehicle it is in and regenerate and rebuild itself at runtime to provide the proper two-way bridge between ROS and the sensed on-board vehicle sensor network. Code generation relies on a per-model defined JSON to map a CAN database (DBC) to the desired ROS topic names and message types. The live ROS publishing of CAN messages allows for instant feedback, and the code regeneration allows for adjustments in DBC or vehicle JSON to iteratively hone in on new vehicle signals. Generated ROS nodes are written in C++ for runtime use in lightweight embedded computers. This has been tested in vehicles from three different Original Equipment Manufacturers (OEMs), and can be extended to support a wide array of vehicles. By using a unifying ROS specification, a heterogeneous set of vehicles can be unified into a fleet with abstracted model-specific details; this opens the door for developing cross-model software applications for vehicle control, connected vehicle applications, or fleet monitoring systems.
An overarching goal of the UNiversal Interoperability for grid-Forming Inverters (UNIFI) Consortium is to develop vendor agnostic specifications and guidelines that ensure interoperability of gridforming (GFM) inverter-based resources (IBRs) without requiring vendors or system operators to reveal proprietary information. These UNIFI principles and specifications are envisioned to apply to a wide range of technologies and systems. The initial work on the UNIFI principles and specifications has focused on performance requirements and high-level aspirational principles outlined in [1]. Going forward, a key question is how to translate such high-level requirements and principles into rigorous specifications that can be enforced and validated for a wide range of IBRs.
Abstract We modify the Zhang‐McFarlane (ZM) deep convection scheme in the Community Atmosphere Model version 5 to couple it with a unified parameterization for boundary‐layer turbulence and shallow convection, that is, Cloud Layers Unified by Binormals (CLUBB). By assuming a lognormal distribution of entrainment rate across the entire moist convective regimes, we link mass fluxes between shallow and deep convection, which are partitioned by the entrainment rate of the shallowest deep convective plume. Hence, a new deep convective closure is established which is coupled to the sub‐grid vertical motion variability in CLUBB. The convection feedback (or memory) effects are also considered to decrease the entrainment spectrum width and enhance the vertical velocity variability that further affect deep convection. Results show that the revised scheme improves the precipitation simulations in terms of the mean state and variability at various timescales, such as the alleviated double‐intertropical convergence zone and more realistic simulations of the seasonal variation of monsoon precipitation over East Asia, Madden‐Julian Oscillation, and precipitation diurnal phase propagations downstream of large terrains. The improvements are still seen in many aspects such as the mean‐state precipitation when turning off the convection feedback impacts in the revised scheme, emphasizing the benefits of using the modified mass‐flux closure. However, the convection feedbacks have considerable effects on the precipitation diurnal cycle simulations over regions with late‐afternoon precipitation peaks. Overall, the revised scheme provides a unified treatment for sub‐grid vertical motions across regimes of boundary‐layer turbulence, shallow convection, and deep convection, leading to better‐simulated precipitation at various timescales.
Research into the early-stage uranium-hydrogen degradation mechanism lacks unified interpretation and understanding due to inadequate temporal and spatial characterization resolution required to unify reaction observations. In this study, white-light interferometry was adapted to characterize the uranium-hydrogen reaction at a temperature of 50 °C and hydrogen pressure of 13.8 kPa. The uranium surface remained unchanged for 62 min during the hydride induction period before the first hydride blister appeared. This blister was monitored from induction through critical spallation and UH₃ powder release during rapid surface hydriding. The critical spallation dimensions measured for height, diameter, area, and perimeter of the first blister were 1.79 µm, 17.58 µm, 187.7 µm 2 and 59.79 µm. Hydriding growth kinetics were assessed by tracking the first hydride site over the reaction time. Analysis shows that the spall-front velocity of the hydride was changing at a rate of 0.91 µm/min which compares favorably with legacy Sievert’s experiments in literature. Total percentage of the surface area hydrided after 242 min was 42.7%, and post-characterization of the UH 3 powders shows both α-UH 3 and β-UH 3 . This work highlights the strength of characterizing the early-stage uranium-hydrogen reaction using white-light interferometry, but more importantly, unifying the understanding and mechanism of the uranium-hydrogen reaction kinetics.
Abstract Multiple electron emission mechanisms often contribute in electron devices, motivating theoretical studies characterizing the transitions between them. Previous studies unified thermionic and field emission, defined by the Richardson-Laue-Dushman (RLD) and Fowler–Nordheim (FN) equations, respectively, with the Child-Langmuir (CL) law for vacuum space-charge limited current (SCLC); another study unified FN and CL with the Mott-Gurney (MG) law for collisional SCLC. However, thermionic emission, which introduces a nonzero injection velocity, may also occur in gas, motivating this analysis to unify RLD, FN, CL, and MG. We exactly calculate the current density as a function of applied voltage over a range of injection velocity (i.e., temperature), mobility, and gap distance. This exact solution approaches RLD, FN, and generalized CL (GCL) and MG (GMG) for nonzero injection velocity under appropriate limits. For nonzero initial velocity, GMG approaches zero for sufficiently small applied voltage and mobility, making these gaps always space-charge limited by either GMG at low voltage or GCL at high voltage. The third-order nexus between FN, GMG, and GCL changes negligibly from the zero initial velocity calculation over ten orders of magnitude of applied voltage. These results provide a closed form solution for GMG and guidance on thermionic emission in a collisional gap.